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Aim Retrospective (pre‐ vs. post‐invasion) and cross‐sectional comparisons of ecosystems exposed to high and low bioinvasion pressure, provide an alternative approach to evaluate shifts in biological communities associated with non‐indigenous species (NIS) introductions. In this study, we aimed to examine general patterns of change in community composition, structure and function in six well‐studied and globally distributed marine ecosystems that had documented histories of biological invasions. Location Global. Methods By considering a range of regional datasets and different sampling approaches, we evaluated trends within and among ecosystems by comparing paired measures of community and functional structure in either space or time. Results Our analyses revealed different patterns of structural and functional change at ecosystem scales, but direct comparisons across regions were hindered by confounding effects of study designs and other drivers of change. The most prominent shifts in community composition were observed in the retrospective studies, characterised by the greatest relative contribution of NIS. No uniform pattern of change in functional metrics was observed across study regions. However, functional evenness and dispersion showed a tendency to increase in systems under higher invasion pressure, refuting the hypothesis of selective accumulation of specific traits and functional homogenisation within ecosystems exposed to high invasion pressure. Main Conclusions Accumulation of NIS within broader communities can be a subtle process, with inherent spatial and temporal variability. Nonetheless, not only do species' proportional contributions to communities change over time in areas subjected to high bioinvasion pressure, but trait profiles can incrementally shift, which alters the original ecology of an area. Planned, long‐term studies that incorporate a range of measures of environmental drivers and ecosystem response are crucial for better understanding of cumulative, community‐level and ecosystem‐scale change associated with biological invasions.

Studying the population structure and genetic diversity of historical datasets is a proposed use for association analysis. This is particularly important when the dataset contains traits that are time-consuming or costly to measure. A set of 96 elite barley genotypes, developed from eight breeding programs of the Western Canadian Cooperative Trials were used in the current study. Genetic diversity, allelic variation, and linkage disequilibrium (LD) were investigated using 5063 high-quality SNP markers via the Illumina 9K Barley Infinium iSelect SNP assay. The distribution of SNPs markers across the barley genome ranged from 449 markers on chromosome 1H to 1111 markers on chromosome 5H. The average polymorphism information content (PIC) per locus was 0.275 and ranged from 0.094 to 0.375. Bayesian clustering in STRUCTURE and principal coordinate analysis revealed that the populations are differentiated primarily due to the different breeding program origins and ear-row type into five subpopulations. Analysis of molecular variance based on PhiPT values suggested that high values of genetic diversity were observed within populations and accounted for 90% of the total variance. Subpopulation 5 exhibited the most diversity with the highest values of the diversity indices, which represent the breeding program gene pool of AFC, AAFRD, AU, and BARI. With increasing genetic distance, the LD values, expressed as r 2 , declined to below the critical r 2 = 0.18 after 3.91 cM, and the same pattern was observed on each chromosome. Our results identified an important pattern of genetic diversity among the Canadian barley panel that was proposed to be representative of target breeding programs and may have important implications for association mapping in the future. This highlight, that efforts to identify novel variability underlying this diversity may present practical breeding opportunities to develop new barley genotypes.

A dataset of historical river discharge into oceans was created using the CaMa-Flood global river routing model and adjusted runoff from the land component of JRA-55. The major rivers were well resolved with a 0.25° horizontal resolution. The total runoff on each drainage basin exhibits a distinctive bias on decadal time scales. The input runoff data were modified using 5-year low-pass-filtered multiplicative factors to fit the annual mean climatology and decadal variations in the reference dataset. The model incorporated data from 1958 to 2016. The yearly and seasonal variations of the major rivers are well represented by the model.

Species distribution models (SDMs) are essential tools to aid conservation biologists in evaluating the combined effects of environmental change and human activities on natural habitats and for the development of relevant conservation plans. However, modeling species distributions over vast and remote regions is often challenging due to poor and heterogeneous data sets, and this raises questions regarding the relevance of the modeling procedures. In recent years, there have been many methodological developments in SDM procedures using virtual species and broad data sets, but few solutions have been proposed to deal with poor or heterogeneous data. In the present work, we address this methodological challenge by studying the performance of different modeling procedures based on 4 real species, using presence-only data compiled from various oceanographic surveys on the Kerguelen Plateau (Southern Ocean). We followed a practical protocol to test for the reliability and performance of the models and to correct for limited and aggregated data, as well as accounting for spatial and temporal sampling biases. Our results show that producing reliable SDMs is feasible as long as the amount and quality of available data allow testing and correcting for these biases. However, we found that SDMs could be corrected for spatial and temporal heterogeneities in only 1 of the 4 species we examined, highlighting the need to consider all potential biases when modeling species distributions. Finally, we show that model reliability and performance also depend on the interaction between the incompleteness of the data and species niches, with the distribution of narrow-niche species being less sensitive to data gaps than species occupying wider niches.

We present the first fully curated, publicly accessible archive of infrasonic records from ten large bolide events documented by the U.S. Air Force Technical Applications Center’s global microbarometer network between 1960 and 1972. Captured on analog strip-chart paper, these waveforms predate modern digital arrays and space-based sensors, making them a unique window on meteoroid activity in the mid-twentieth century. Prior studies drew important scientific conclusions from the records but released only limited artifacts, chiefly period–amplitude tables and unprocessed scans, leaving the underlying data inaccessible for independent study. The present release transforms those limited excerpts into a research-ready resource. By capturing ten large events in the mid-20th century, the dataset constitutes a critical reference point for assessing bolide activity before the advent of modern space-based and digital ground-based monitoring. The multi-year coverage and worldwide distribution of events provide a valuable reference for comparing past and more recent detections, facilitating assessments of long-term flux and the dynamics of acoustic wave propagation in Earth’s atmosphere. The dataset’s availability in a consolidated format ensures straightforward access to waveforms and derived measurements, supporting a wide range of scientific inquiries into bolide physics and infrasound monitoring. By preserving these historical acoustic observations, the collection maintains a significant record of mid-20th-century meteoroid entries. It thereby establishes a basis for further refinement of impact hazard evaluations, contributes to historical continuity in atmospheric observation, and enriches the study of meteoroid-generated infrasound signals on a global scale.

The latest developments in the field of digital humanities have increasingly enabled the construction of large data sets which can be easily accessed and used. These data sets often contain indirect spatial information, such as historical addresses. Historical geocoding is the process of transforming indirect spatial information into direct locations which can be placed on a map, thus allowing for spatial analysis and cross-referencing. There are many geocoders that work efficiently for current addresses. However, these do not tackle temporal information, and usually follow a strict hierarchy (country, city, street, house number, etc.) which is difficult—if not impossible—to use with historical data. Historical data is filled with uncertainty (pertaining to temporal, textual, and positional accuracy, as well as to the reliability of historical sources) which can neither be ignored nor entirely resolved. Our open source, open data, and extensible solution for geocoding is based on extracting a large number of simple gazetteers composed of geohistorical objects, from historical maps. Geocoding a historical address becomes the process of finding one or several geohistorical objects in the gazetteers which best match the historical address searched by the user. The matching criteria are customisable, weighted, and include several dimensions (fuzzy string, fuzzy temporal, level of detail, positional accuracy). Since our goal is to facilitate historical work, we also put forward web-based user interfaces which help geocode (one address or batch mode) and display results over current or historical maps. Geocoded results can then be checked and edited collaboratively (no source is modified). The system was tested on the city of Paris, France, for the 19th and 20th centuries. It showed high response rates and worked quickly enough to be used interactively.

Large sample sizes are crucial for accurately capturing spatial changes in soil properties by spatial interpolation methods. However, soil bulk density (BD) data in historical datasets is often incomplete, and it’s uncertain if filled values enhance spatial interpolation accuracy. Using 2,883 cropland soil BD samples from the Sichuan Basin in China, we developed the best prediction models from traditional pedotransfer function (PTF), multiple linear regression (MLR), random forest (RF), and radial basis function neural network (RBFNN) to fill missing BD values for 1,336 samples. We then applied ordinary kriging (OK) and inverse distance weighting (IDW) to map soil BD, incorporating the filled BD as modeling points. The RBFNN model, tailored for each sub-watershed, yielded the highest accuracy in filling missing BD, with an increase in coefficient of determination ( R 2 ) by 19.54–37.36% and reductions in mean absolute error (MAE), mean relative error (MRE) and root mean square error (RMSE) by 8.91–14.81%, 9.02–16.22% and 7.71–13.61%, respectively. Incorporating filled BD data reduced the MAE, MRE, and RMSE of OK and IDW by 4.17%, 4.36%, 4.96%, and 6.54%, 6.92%, 8.15%, respectively, significantly lowering spatial interpolation uncertainty. This methodology improves the accuracy of soil property mapping in regions with incomplete historical data.

The age of a historical manuscript can be an invaluable source of information for paleographers and historians. The process of automatic manuscript age detection has inherent complexities, which are compounded by the lack of suitable datasets for algorithm testing. This paper presents a dataset of historical handwritten Arabic manuscripts designed specifically to test state-of-the-art authorship and age detection algorithms. Qatar National Library has been the main source of manuscripts for this dataset while the remaining manuscripts are open source. The dataset consists of over 2000 images taken from various handwritten Arabic manuscripts spanning fourteen centuries. In addition, a sparse representation-based approach for dating historical Arabic manuscript is also proposed. There is lack of existing datasets that provide reliable writing date and author identity as metadata. KERTAS is a new dataset of historical documents that can help researchers, historians and paleographers to automatically date Arabic manuscripts more accurately and efficiently.

One recent study by using instrumental records suggested the correlation between East Asian surface air temperatures (EATs) and the Atlantic Multidecadal Oscillation (AMO) reaches the maximum when the former leads the latter by 5–7 years. This seems to disagree with a previous well-realized point that the AMO modulates the decadal variation of EATs, since the atmosphere responds swiftly to sea surface temperature anomalies (SSTA) if therein. It implies that the AMO-EATs correlation should reach the maximum when they are simultaneous or the AMO leads EATs slightly, rather than that the EATs lead the AMO. Thus, this poses an issue about the reality of the newly found lead-lag correlation. Because the instrumental record in the natural climate system may be contaminated by human activities, the EATs-AMO lead-lag correlation derived from the instrumental records may not be a realistic connection of the natural climate system. Thus, whether the connection also exists in the proxies prior to the industrial is essential to answer the issue. In this study the EATs-AMO lead-lag connection is analyzed by using the reconstructed data in the last 500 years, together with the control experimental data with the prescribed pre-industrial forcing in a multiple of coupled climate system models, which attend the international CMIP5 program. The results suggest that the connection, the EATs lead the AMO, also exists in the period from the Little Ice Age (LIA) to the industrial, 1500–1860AD. Therefore, the connection may be realistic in the natural climate system. The mechanisms for the connection are then discussed briefly. The results from this paper provide some insights into the connection of the AMO with East Asian climate.

This article describes the digitization of a series of historical datasets based οn the reports of the 1908–1910 Danish Oceanographical Expeditions to the Mediterranean and adjacent seas. All station and sampling metadata as well as biodiversity data regarding calcareous rhodophytes, pelagic polychaetes, and fish (families Engraulidae and Clupeidae) obtained during these expeditions were digitized within the activities of the LifeWatchGreece Research Ιnfrastructure project and presented in the present paper. The aim was to safeguard public data availability by using an open access infrastructure, and to prevent potential loss of valuable historical data on the Mediterranean marine biodiversity. The datasets digitized here cover 2,043 samples taken at 567 stations during a time period from 1904 to 1930 in the Mediterranean and adjacent seas. The samples resulted in 1,588 occurrence records of pelagic polychaetes, fish (Clupeiformes) and calcareous algae (Rhodophyta). In addition, basic environmental data (e.g. sea surface temperature, salinity) as well as meterological conditions are included for most sampling events. In addition to the description of the digitized datasets, a detailed description of the problems encountered during the digitization of this historical dataset and a discussion on the value of such data are provided.