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Large‐scale biodiversity databases have great potential for quantifying long‐term trends of species, but they also bring many methodological challenges. Spatial bias of species occurrence records is well recognized. Yet, the dynamic nature of this spatial bias – how spatial bias has changed over time – has been largely overlooked. We examined the spatial bias of species occurrence records within multiple biodiversity databases in Germany and tested whether spatial bias in relation to land cover or land use (urban and protected areas) has changed over time. We focused our analyses on urban and protected areas as these represent two well‐known correlates of sampling bias in biodiversity datasets. We found that the proportion of annual records from urban areas has increased over time while the proportion of annual records within protected areas has not consistently changed. Using simulations, we examined the implications of this changing sampling bias for estimation of long‐term trends of species' distributions. When assessing biodiversity change, our findings suggest that the effects of spatial bias depend on how it affects sampling of the underlying land‐use change drivers affecting species. Oversampling of regions undergoing the greatest degree of change, for instance near human settlements, might lead to overestimation of the trends of specialist species. For robust estimation of the long‐term trends in species' distributions, analyses using species occurrence records may need to consider not only spatial bias, but also changes in the spatial bias through time.

Citizen scientists play an increasingly important role in biodiversity monitoring. Most of the data, however, are unstructured—collected by diverse methods that are not documented with the data. Insufficient understanding of the data collection processes presents a major barrier to the use of citizen science data in biodiversity research. We developed a questionnaire to ask citizen scientists about their decision-making before, during and after collecting and reporting species observations, using Germany as a case study. We quantified the greatest sources of variability among respondents and assessed whether motivations and experience related to any aspect of data collection. Our questionnaire was answered by almost 900 people, with varying taxonomic foci and expertise. Respondents were most often motivated by improving species knowledge and supporting conservation, but there were no linkages between motivations and data collection methods. By contrast, variables related to experience and knowledge, such as membership of a natural history society, were linked with a greater propensity to conduct planned searches, during which typically all species were reported. Our findings have implications for how citizen science data are analysed in statistical models; highlight the importance of natural history societies and provide pointers to where citizen science projects might be further developed.

Tropical montane forest ecosystems are pivotal for sustaining biodiversity and essential terrestrial ecosystem services, including the provision of high-quality fresh water. Nonetheless, the impact of montane deforestation and climate change on the capacity of forests to deliver ecosystem services is yet to be fully understood. In this study, we offer observational evidence demonstrating the response of air temperature and cloud base height to deforestation in African montane forests over the last two decades. Our findings reveal that approximately 18% (7.4 ± 0.5 million hectares) of Africa’s montane forests were lost between 2003 and 2022. This deforestation has led to a notable increase in maximum air temperature (1.37 ± 0.58 °C) and cloud base height (236 ± 87 metres), surpassing shifts attributed solely to climate change. Our results call for urgent attention to montane deforestation, as it poses serious threats to biodiversity, water supply, and ecosystem services in the tropics. The authors show that recent deforestation induced more warming and cloud level rise than that caused by climate change, threatening biodiversity and water supply in African montane forests.

The East African mountain ecosystems are facing increasing threats due to global change, putting their unique socio-ecological systems at risk. To monitor and understand these changes, researchers and stakeholders require accessible analysis-ready remote sensing data. Although satellite data is available for many applications, it often lacks accurate geometric orientation and has extensive cloud cover. This can generate misleading results and make it unreliable for time-series analysis. Therefore, it needs comprehensive processing before usage, which encompasses multi-step operations, requiring large computational and storage capacities, as well as expert knowledge. Here, we provide high-quality, atmospherically corrected, and cloud-free analysis-ready Sentinel-2 imagery for the Bale Mountains (Ethiopia), Mounts Kilimanjaro and Meru (Tanzania) ecosystems in East Africa. Our dataset ranges from 2017 to 2021 and is provided as monthly and annual aggregated products together with 24 spectral indices. Our dataset enables researchers and stakeholders to conduct immediate and impactful analyses. These applications can include vegetation mapping, wildlife habitat assessment, land cover change detection, ecosystem monitoring, and climate change research.

Background Ticks are the primary vectors of numerous zoonotic pathogens, transmitting more pathogens than any other blood-feeding arthropod. In the northern hemisphere, tick-borne disease cases in humans, such as Lyme borreliosis and tick-borne encephalitis, have risen in recent years, and are a significant burden on public healthcare systems. The spread of these diseases is further reinforced by climate change, which leads to expanding tick habitats. Switzerland is among the countries in which tick-borne diseases are a major public health concern, with increasing incidence rates reported in recent years. Methods In response to these challenges, the “Tick Prevention” app was developed by the Zurich University of Applied Sciences and operated by A&K Strategy Ltd. in Switzerland. The app allows for the collection of large amounts of data on tick attachment to humans through a citizen science approach. In this study, citizen science data were utilized to map tick attachment to humans in Switzerland at a 100 m spatial resolution, on a monthly basis, for the years 2015 to 2021. The maps were created using a state-of-the-art modeling approach with the software extension spatialMaxent, which accounts for spatial autocorrelation when creating Maxent models. Results Our results consist of 84 maps displaying the risk of tick attachments to humans in Switzerland, with the model showing good overall performance, with median AUC ROC values ranging from 0.82 in 2018 to 0.92 in 2017 and 2021 and convincing spatial distribution, verified by tick experts for Switzerland. Our study reveals that tick attachment to humans is particularly high at the edges of settlement areas, especially in sparsely built-up suburban regions with green spaces, while it is lower in densely urbanized areas. Additionally, forested areas near cities also show increased risk levels. Conclusions This mapping aims to guide public health interventions to reduce human exposure to ticks and to inform the resource planning of healthcare facilities. Our findings suggest that citizen science data can be valuable for modeling and mapping tick attachment risk, indicating the potential of citizen science data for use in epidemiological surveillance and public healthcare planning. Graphical Abstract

This study integrates geospatial modeling with multi-criteria decision analysis for an improved approach to landslide susceptibility mapping (LSM). This approach addresses key challenges in LSM through sophisticated multicollinearity analysis and machine learning strategies. We compared three machine learning models for weighting, and of them the Permutation-Weighted model yielded the best prediction results, with an Area Under Curve (AUC) of 95%, an accuracy of 69%, and a recall of 66%. To resolve perfect multicollinearity (r = 1) between land use land cover (LULC) and geological factors, we implemented Principal Component Analysis (PCA). The selected factors demonstrated strong predictive power, with the PCA-derived features exhibiting the best performance, having a Variation Inflation Factor (VIF) of 1.004. Slope appeared as the most influential factor (51.7% contribution), while the Topographic Wetness Index (TWI) was less dominant with only 6.6%. Multiple landslide susceptibility mapping methods yielded consistent results, with 29.8–30.1% of the study area showing moderate susceptibility and 35.2–36.9% in the high to very high susceptibility class. The model also incorporated vulnerability parameters weighted by the United Nations Office for Disaster Risk Reduction (UNDRR) indicators, including farmland, buildings, bare land, water bodies, roads, and amenities to generate hazard, vulnerability, and risk maps. The results were verified through visual comparison with high-resolution Google Earth imagery. The Permutation-Weighted model performed better than others, categorizing 12.4% at high-risk, while Random Forest (RF) categorized 7.2% at high risk. This study makes three key contributions: (1) It establishes the effectiveness of PCA/VIF for variable selection, (2) it provides a comparison of machine learning weighting techniques, and (3) it validates a workflow applicable to data-scarce regions.

Any program aimed at improving rice quality must investigate and comprehend the variety and heterogeneity of germplasm genotypes. Genetic variability analysis aids the breeders in determining the best criteria and selection procedure to be utilized in order to enhance the desired attributes. Twenty genetic materials were used in a two‐year (2021‐2022) field experiment at the G.P. Koirala College of Agriculture and Research Center in Morang, Nepal. This study aimed to determine the genetic diversity by principal component analysis (PCA) and cluster analysis, as well as to assess the path coefficient (PC), genetic deviation, and character association based on grain yield (GY) and other yield‐attributing variables. For all the parameters under study, variance analysis across all genotypes showed substantial differences ( P < 0.001), suggesting a higher level of genetic variability for selection purposes. The higher phenotypic and genotypic coefficient of variation (PCV and GCV) were observed for yield‐related traits, including grains per panicle (GP), straw yield (SY), harvest index (HI), days to flowering (DF), and test weight (TW). Higher genetic advance as a percentage of the mean (GAM) and higher heritability () for each attribute indicated nonadditive gene activity and highlighted that selection would likely be successful. According to the correlation analysis, selection based on days to maturity (DM), TW, HI, tiller per m 2 (TM), and effective tiller per hill (ETH) will be beneficial for raising rice GY. The results of PCA and PC indicated that the direct selection of DM, ETH, TW, and HI would be productive in improving the GY of rice in future breeding projects. The results of the cluster analysis divided the 20 rice genotypes into seven groups, with Cluster VII with genotype Swarna Sub‐1 for GY and HI; Cluster I with genotypes NR2188‐13‐5‐2‐5‐1, Radha‐13, and Bas Dhan for PH and SY; Cluster VI with genotypes NR2187‐33‐1‐2‐1‐1‐1 and Ranjeet for DF and DM; and Cluster V with genotypes NR2182‐33‐3‐2‐1‐1‐1 and NR2192‐16‐1‐1‐1‐1 for TM and PL being chosen as the best ones. Upon confirmation, these genotypes can be recommended for commercial release or used as prospective breeding material in cross‐breeding initiatives aimed at producing cultivars possessing desirable homogeneous features.

The panicle architecture is a critical determinant of the reproductive success of rice plants and has a direct impact on grain production. In this study, we evaluated the genetic variation of panicle parameters in fifteen rice genotypes and compared them with five commonly grown cultivars in the study area to assess their potential for crop improvement initiatives. The selection of the 15 genotypes was based on specific criteria, including diversity in origin, grain type, and adaptation to local conditions. Significant morphological variations were observed among the rice accessions for panicle parameters, including panicle length, weight, test weight, panicle number, grains/panicle, chaffs/panicle, and flag leaf area. Principal component analysis (PCA) revealed that the first two axes explained 59.8% of the total variance, indicating substantial variability in panicle features across the genotypes. Panicle length, panicle weight, and flag leaf area were identified as significant variables contributing to phenotypic variation. Multiple correlation analysis indicated that panicle weight was strongly positively correlated with panicle length, flagleaf area, grains/panicle, and test weight but was negatively correlated with panicle number, chaffs/panicle, and panicle angle. Genetic advance as a percentage of the mean (GAM) ranged from 7.226% for panicle number to 70.728% for chaffs/panicle. Traits such as grain/panicle, panicle length, flag leaf area, chaffs/panicle, and test weight exhibited high GAM and heritability, highlighting their significance for selection during crop improvement. Certain rice accessions, namely SVIN123, IR106523-25-34-3-2-13-1-2, Radha-13, and IR15L17315, demonstrated superior panicle weight, larger grains/panicle, and panicle length, making them attractive candidates for future rice breeding projects.

Rice production can be severely affected by drought stress and this could cause massive economic losses every year. Global climate change is steadily becoming an important issue. This research was conducted in order to identify drought-tolerant rice genotypes using stress tolerance indices. Employing a randomized complete block design, a total of nine rice genotypes were assessed under irrigated and drought-stress conditions from June to November 2022 at the Institute of Agriculture and Animal Science (IAAS), Paklihawa, Nepal. In particular, the stress susceptibility index (SSI), mean productivity (MP), and geometric mean productivity (GMP) revealed strong and highly significant positive correlations to agricultural yields under both irrigated and drought stress conditions. The stress tolerance index (STI) and yield stability index (YSI) showed strong and highly significant positive correlations to yield under drought conditions while the tolerance index (TOL) and yield index (YI) showed strong and negative significant associations to yield under stress conditions. The highest STI, GMP, and MP were observed in the IR16L1713 genotype followed by IR17L1387, establishing these two as the steadiest and most efficient genotypes among nine genotypes of rice. These genotypes have the potential to be selected for maximum outputs under both irrigated and drought-stress situations. A biplot analysis showed a positive association of MP, GMP, and YI to rice yields in an irrigated environment and a negative correlation of SSI, STI, and TOL, with a reduction percentage in a drought-stressed environment. Therefore, these stress indicators can be used to evaluate rice genotypes under both normal and drought stress settings. La producción de arroz podría verse gravemente afectada por el estrés provocado por la sequía, lo que podría causar enormes pérdidas económicas cada año. El problema del cambio climático global se está convirtiendo cada vez más en una cuestión importante. Esta investigación se llevó a cabo con el fin de identificar los genotipos de arroz tolerantes a la sequía utilizando índices de tolerancia al estrés. Empleando un diseño de bloques completos aleatorizados, se evaluaron un total de nueve genotipos de arroz en condiciones de riego y estrés por sequía de junio a noviembre de 2022 en el Instituto de Agricultura y Ciencia Animal (IAAS), Paklihawa, Nepal. En particular, el índice de susceptibilidad al estrés (ISE), la productividad media (PM) y la productividad media geométrica (PMG) revelaron correlaciones positivas fuertes y altamente significativas con el rendimiento tanto en condiciones de riego como de estrés por sequía. Asimismo, el índice de tolerancia al estrés (ITS) y el índice de estabilidad del rendimiento (IER) mostraron correlaciones positivas fuertes y altamente significativas con el rendimiento en condiciones de sequía, mientras que el índice de tolerancia (TOL) y el índice de rendimiento (IR) mostraron asociaciones significativas fuertes y negativas con el rendimiento en condiciones de estrés. Los mayores ITS, PMG y PM se observaron en IR16L1713, seguido por IR17L1387, estableciéndolos como los genotipos más estables y eficientes entre nueve genotipos de arroz. Estos genotipos tienen el potencial de ser seleccionados para una producción abundante tanto en condiciones de riego como de estrés por sequía. Un análisis biplot mostró una asociación positiva de PM, PMG e IR con el rendimiento en un ambiente irrigado y una correlación negativa de ISS, ITS y TOL con reducciones en el porcentaje en un ambiente de estrés por sequía. Por lo tanto, estos indicadores de estrés se pueden utilizar para evaluar genotipos de arroz tanto en condiciones normales como de estrés por sequía.