Global arthropod beta-diversity is spatially and temporally structured by latitude.

Global biodiversity gradients are generally expected to reflect greater species replacement closer to the equator. However, empirical validation of global biodiversity gradients largely relies on vertebrates, plants, and other less diverse taxa. Here we assess the temporal and spatial dynamics of global arthropod biodiversity dynamics using a beta-diversity framework. Sampling includes 129 sampling sites whereby malaise traps are deployed to monitor temporal changes in arthropod communities. Overall, we encountered more than 150,000 unique barcode index numbers (BINs) (i.e. species proxies). We assess between site differences in community diversity using beta-diversity and the partitioned components of species replacement and richness difference. Global total beta-diversity (dissimilarity) increases with decreasing latitude, greater spatial distance and greater temporal distance. Species replacement and richness difference patterns vary across biogeographic regions. Our findings support long-standing, general expectations of global biodiversity patterns. However, we also show that the underlying processes driving patterns may be regionally linked.

Performance prediction of polymer-fullerene organic solar cells and data mining-assisted designing of new polymers.

CONTEXT: Selecting high performance polymer materials for organic solar cells (OSCs) remains a compelling goal to improve device morphology, stability, and efficiency. To achieve these goals, machine learning has been reported as a powerful set of algorithms/techniques to solve complex problems and help/guide exploratory researchers to screen, map, and develop high performance materials. In present work, we have applied machine learning tools to screen data from reported studies and designed new polymer acceptor materials, respectively. Quantitative structure-activity relationship (QSAR) models were generated using machine learning-assisted simulation techniques. For this purpose, 3000 molecular descriptors are generated. Consequently, molecular descriptors having key effect on power conversion efficiency (PCE) were identified. Moreover, numerous regression models (e.g., random forest and bagging regressor models) were developed to predict the PCE. In particular, new materials were designed based on the similarity analysis. The GDB17 chemical database consisting of 166 million organic molecules in an ordered form is used for performing similarity analysis. A similarity behavior between GDB17 materials and the materials reported in literature is studied using RDKit (a cheminformatics software). Noteworthily, 100 monomers proved to be unique and effective, and PCEs of these monomers are predicted. Among these monomers, four monomers exhibited PCE higher than 14%, which is better than various reported studies. Our methodology provides a unique, time- and cost-efficient approach to screening and designing new polymers for OSCs using similarity analysis without revisiting the reported studies. METHODS: To perform machine learning analysis, data from reported studies and online databases was collected. Different molecular descriptors were generated for polymer materials utilizing Dragon software. 3D structures of studied molecules were applied as input (SDF; structure data file format). Importantly, about 3000 molecular descriptors were generated. Comma-separated value (.csv) file format was used to export these molecular descriptors. To shortlist best descriptors, univariate regression analysis was performed. These descriptors were further utilized for training machine learning models. Moreover, necessary packages of Python for data analysis and visualization were imported such as Matplotlib, Numpy, Pandas, Scikit-learn, Seaborn, and Scipy. Random forest and bagging regressor models were applied for performing machine learning analysis. A cheminformatics software, RDKit, was applied for similarity analysis.

Integrating traditional ecological knowledge into habitat restoration: implications for meeting forest restoration challenges.

BACKGROUND: Traditional ecological knowledge (TEK) helps tribal communities adapt to socio-ecological changes, improving the long-term sustainability of their livelihood strategies and fostering social-ecological resilience. TEK provides thorough understanding of ecosystem dynamics, as well as how they relate to societal norms, practices, and resource use patterns. The integrity of TEK is often in jeopardy due to changes in belief systems, regional languages, traditional ways of subsistence, and disruption of traditional social-ecological systems. Landscape restoration has the ability to promote self-determination while safeguarding the livelihoods, beliefs, cultural, and biodiversity of indigenous peoples. However, there is a substantial knowledge gap on how TEK might aid ecosystem restoration, particularly in elephant corridors. METHODS: The current study focused on gathering traditional ecological knowledge on the woody tree species from the Dering-Dibru Saikhowa Elephant Corridor using semi-structured interviews, group discussions, and direct observations. The acquired data were applied to heat map cluster analysis and ordination techniques using R software version 4.0.0. RESULTS: Traditional usage information of 31 tree species utilized for food, fodder, timber, fuelwood, medicinal, and livelihood by local people was gathered. Most of the species utilized locally belonged to the families Combretaceae and Fabaceae. The species were classified into single, double, or multi-uses based on the extent of utilization. Azadirachta indica, Phyllanthus emblica, and Syzygium cumini (six each) had the highest utilization, while Mesua ferrea had the lowest. Chionanthus ramiflorus, Artocarpus heterophyllus, and Dillenia indica were among the plants valuable to wildlife, providing both forage and habitat for a wide variety of birds and animals. Artocarpus heterophyllus, Averrhoa carambola, Mangifera indica, P. emblica, Psidium guajava, and S. cumini were among the plants important for the livelihoods of the local community. Our findings demonstrated that local people were knowledgeable about the plant species to use as pioneer species, such as Bombax ceiba, Albizia lebbeck, D. indica, S. cumini, P. emblica, Lagerstroemia speciosa, and Alstonia scholaris, for habitat restoration in a diverse habitat. We classified the habitat of the enlisted species into different categories, and two clusters (clusters 1 and 2) were identified based on the similarity of woody species in different habitats. We prioritized multiple tree species for eco-restoration using the information collected through TEK. We planted 95,582 saplings on 150 hectares in the Dering-Dibru Saikhowa Elephant Corridors' degraded habitat patches, which will serve as future reference site for landscape rehabilitation. Out of total saplings planted, 56% of the species were linked to native communities through ethnobotanical uses, as well as providing connectivity and habitat for elephant movement, 16% of all woody species are pioneer species to colonize a degraded habitat, 15% of all woody species are preferred food and foraging by wildlife, and 13% of the species as a source of livelihood for local people, incorporating social, economic, cultural, and biodiversity benefits into the restoration framework. CONCLUSION: The current study also provides insights how the TEK can assist with aspects of ecological restoration, from reference ecosystem reconstruction and adaptive management through species selection for restoration, monitoring, and evaluation of restoration effectiveness.

Insect diversity in the Saharo-Arabian region: Revealing a little-studied fauna by DNA barcoding.

Although insects dominate the terrestrial fauna, sampling constraints and the poor taxonomic knowledge of many groups have limited assessments of their diversity. Passive sampling techniques and DNA-based species assignments now make it possible to overcome these barriers. For example, Malaise traps collect specimens with minimal intervention while the Barcode Index Number (BIN) system automates taxonomic assignments. The present study employs Malaise traps and DNA barcoding to extend understanding of insect diversity in one of the least known zoogeographic regions, the Saharo-Arabian. Insects were collected at four sites in three countries (Egypt, Pakistan, Saudi Arabia) by deploying Malaise traps. The collected specimens were analyzed by sequencing 658 bp of cytochrome oxidase I (DNA barcode) and assigning BINs on the Barcode of Life Data Systems. The year-long deployment of a Malaise trap in Pakistan and briefer placements at two Egyptian sites and at one in Saudi Arabia collected 53,092 specimens. They belonged to 17 insect orders with Diptera and Hymenoptera dominating the catch. Barcode sequences were recovered from 44,432 (84%) of the specimens, revealing the occurrence of 3,682 BINs belonging to 254 families. Many of these taxa were uncommon as 25% of the families and 50% of the BINs from Pakistan were only present in one sample. Family and BIN counts varied significantly through the year, but diversity indices did not. Although more than 10,000 specimens were analyzed from each nation, just 2% of BINs were shared by Pakistan and Saudi Arabia, 4% by Egypt and Pakistan, and 7% by Egypt and Saudi Arabia. The present study demonstrates how the BIN system can circumvent the barriers imposed by limited access to taxonomic specialists and by the fact that many insect species in the Saharo-Arabian region are undescribed.