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Spatial patterns of biodiversity are inextricably linked to their collection methods, yet no synthesis of bias patterns or their consequences exists. As such, views of organismal distribution and the ecosystems they make up may be incorrect, undermining countless ecological and evolutionary studies. Using 742 million records of 374 900 species, we explore the global patterns and impacts of biases related to taxonomy, accessibility, ecotype and data type across terrestrial and marine systems. Pervasive sampling and observation biases exist across animals, with only 6.74% of the globe sampled, and disproportionately poor tropical sampling. High elevations and deep seas are particularly unknown. Over 50% of records in most groups account for under 2% of species and citizen‐science only exacerbates biases. Additional data will be needed to overcome many of these biases, but we must increasingly value data publication to bridge this gap and better represent species' distributions from more distant and inaccessible areas, and provide the necessary basis for conservation and management.

International agreements aim to conserve 17% of Earth's land area by 2020 but include no area‐based conservation targets within the working landscapes that support human needs through farming, ranching, and forestry. Through a review of country‐level legislation, we found that just 38% of countries have minimum area requirements for conserving native habitats within working landscapes. We argue for increasing native habitats to at least 20% of working landscape area where it is below this minimum. Such target has benefits for food security, nature's contributions to people, and the connectivity and effectiveness of protected area networks in biomes in which protected areas are underrepresented. We also argue for maintaining native habitat at higher levels where it currently exceeds the 20% minimum, and performed a literature review that shows that even more than 50% native habitat restoration is needed in particular landscapes. The post‐2020 Global Biodiversity Framework is an opportune moment to include a minimum habitat restoration target for working landscapes that contributes to, but does not compete with, initiatives for expanding protected areas, the UN Decade on Ecosystem Restoration (2021–2030) and the UN Sustainable Development Goals.

Large-scale observational data from citizen science efforts are becoming increasingly common in ecology, and researchers often choose between these and data from intensive local-scale studies for their analyses. This choice has potential trade-offs related to spatial scale, observer variance, and interannual variability. Here we explored this issue with phenology by comparing models built using data from the large-scale, citizen science USA National Phenology Network (USA-NPN) effort with models built using data from more intensive studies at Long Term Ecological Research (LTER) sites. We built statistical and process based phenology models for species common to each data set. From these models, we compared parameter estimates, estimates of phenological events, and out-of-sample errors between models derived from both USA-NPN and LTER data. We found that model parameter estimates for the same species were most similar between the two data sets when using simple models, but parameter estimates varied widely as model complexity increased. Despite this, estimates for the date of phenological events and out-of-sample errors were similar, regardless of the model chosen. Predictions for USA-NPN data had the lowest error when using models built from the USA-NPN data, while LTER predictions were best made using LTER-derived models, confirming that models perform best when applied at the same scale they were built. This difference in the cross-scale model comparison is likely due to variation in phenological requirements within species. Models using the USA-NPN data set can integrate parameters over a large spatial scale while those using an LTER data set can only estimate parameters for a single location. Accordingly, the choice of data set depends on the research question. Inferences about species-specific phenological requirements are best made with LTER data, and if USA-NPN or similar data are all that is available, then analyses should be limited to simple models. Large-scale predictive modeling is best done with the larger-scale USA-NPN data, which has high spatial representation and a large regional species pool. LTER data sets, on the other hand, have high site fidelity and thus characterize inter-annual variability extremely well. Future research aimed at forecasting phenology events for particular species over larger scales should develop models that integrate the strengths of both data sets.

Background Despite the presence of a large number of toxic components, primarily juglone, in walnut green husks, these components have failed to prevent infestations of the specialized pest Atrijuglans aristata. At present, it remains unclear whether detoxification genes play a pivotal role in enhancing host fitness of A. aristata . In this study, we explored the adaptation mechanisms of A. aristata to host plants by identifying and expressing gene families associated with detoxification, as well as assessing the binding affinity of their protein products with juglone. Results We identified 84 P450 (P450 monooxygenases), 48 COE (carboxylesterases), 34 GST (glutathione S-transferases), 26 UGT (UDP-glycosyltransferases), and 57 ABC (ATP-binding cassette) transporter genes in the genome of A. aristata . The P450 gene family of A. aristata was divided into four classes based on phylogenetic relationships. Comparative transcriptome analysis revealed that 383 genes in the larval guts of A. aristata were significantly down-regulated after starvation treatment compared with normal feeding. These genes were frequently enriched in pathways related to P450 detoxification metabolism. Through homology modeling and molecular docking analysis of the 12 significantly down-regulated P450 genes, it was found that all 12 proteins exhibited strong binding affinities with the ligand molecule juglone. Conclusions The gene number of the detoxification-related families in the A. aristata genome is close to that of other specialized insect species. Twelve candidate P450 genes identified in comparative transcriptome analysis are inferred to be involved in host adaptation of A. aristata. These results provide a theoretical basis for the management of walnut pests.

Pollen is the primary protein and nutrient source for bees and they employ many different behaviors to gather it. Numerous terms have been coined to describe pollen gathering behaviors, creating confusion as many are not clearly-defined or overlap with existing terms. There is a need for a clear yet flexible classification that enables accurate, succinct descriptions of pollen gathering behaviors to enable meaningful discussion and comparison. Here, we classify the different pollen gathering behaviors into two main classes: active and incidental pollen collection. Active pollen collection is subdivided into six behaviors: scraping with the extremities, buzzing, rubbing with the body and/or scopae, rubbing with the face, tapping, and rasping. In addition to the active and incidental pollen gathering behaviors, many bees have an intermediate step in which they temporarily accumulate pollen on a discrete patch of specialized hairs. Each behavior is described and illustrated with video examples. Many of these behaviors can be further broken down based on the variations found in different bee species. Different species or individual bees mix and match these pollen collecting behaviors depending on their behavioral plasticity and host plant morphology. Taken together, the different behaviors are combined to create complex behavioral repertoires built on a foundation of simple and basic steps. This classification sets the groundwork for further research on various topics, including behavioral plasticity in different species, comparisons between generalists and specialists, and the relative effectiveness of different pollen gathering behaviors.

Wildlife trade is a major driver of biodiversity loss, yet whilst the impacts of trade in some species are relatively well-known, some taxa, such as many invertebrates are often overlooked. Here we explore global patterns of trade in the arachnids, and detected 1,264 species from 66 families and 371 genera in trade. Trade in these groups exceeds millions of individuals, with 67% coming directly from the wild, and up to 99% of individuals in some genera. For popular taxa, such as tarantulas up to 50% are in trade, including 25% of species described since 2000. CITES only covers 30 (2%) of the species potentially traded. We mapped the percentage and number of species native to each country in trade. To enable sustainable trade, better data on species distributions and better conservation status assessments are needed. The disparity between trade data sources highlights the need to expand monitoring if impacts on wild populations are to be accurately gauged and the impacts of trade minimised. Trade in arachnids includes millions of individuals and over 1264 species, with over 70% of individuals coming from the wild.

Recent advances in DNA barcoding have immeasurably advanced global biodiversity research in the last two decades. However, inherent limitations in barcode sequences, such as hybridization, introgression or incomplete lineage sorting can lead to misidentifications when relying solely on barcode sequences. Here, we propose a new Niche‐model‐Based Species Identification (NBSI) method based on the idea that species distribution information is a potential complement to DNA barcoding species identifications. NBSI performs species membership inference by incorporating niche modelling predictions and traditional DNA barcoding identifications. Systematic tests across diverse scenarios show significant improvements in species identification success rates under the newly proposed NBSI framework, where the largest increase is from 4.7% (95% CI: 3.51%–6.25%) to 94.8% (95% CI: 93.19%–96.06%). Additionally, obvious improvements were observed when using NBSI on potentially ambiguous sequences whose genetic nearest neighbours belongs to another species or more than two species, which occurs commonly with species represented by single or short DNA barcodes. These results support our assertion that environmental factors/variables are valuable complements to DNA sequence data for species identification by avoiding potential misidentifications inferred from genetic information alone. The NBSI framework is currently implemented as a new R package, ‘NicheBarcoding’, that is open source under GNU General Public Licence and freely available from https://CRAN.R‐project.org/package=NicheBarcoding. 摘要 近二十年来，DNA条形码技术的发展极大推进了全球生物多样性的研究进程。然而，条形码序列固有的局限性，如杂交、基因渐渗或不完全谱系分选，可能导致仅依赖条形码序列进行物种鉴定时出现错误。 在本文中，我们提出了一种基于生态位模型的物种鉴定新方法（NBSI），该方法认为物种的分布信息是DNA条形码物种鉴定的潜在补充。NBSI将生态位模型的预测与传统DNA条形码鉴定相整合，从而推断未知样本所属的物种。 通过对多种情况的数据集进行系统性测试，结果显示在新提出的NBSI框架下，物种鉴定成功率显著提高，其中提升幅度最大的是从4.7%（95%CI：3.51%‐6.25%）提高至 94.8%（95%CI：93.19%‐96.06%）。此外，在使用NBSI处理潜在错误序列时也观察到了明显的改进，这些序列的遗传最近邻体属于除它本身之外的另一个物种或属于两个以上的物种，这类情况在单序列物种或短序列物种数据集中很常见。 上述结果支持了我们的观点，即环境因素/变量可以作为DNA序列数据的有益补充，通过避免仅依赖遗传信息得出的潜在错误判断来进行物种鉴定。NBSI框架目前已编写为一个新的R语言函数包“NicheBarcoding”实现，该包在GNU通用公共许可证下开源，并且可以从https://CRAN.R‐project.org/package=NicheBarcoding免费获取。

Environmental factors can influence ecological networks, but these effects are poorly understood in the realm of the phylogeny of host-parasitoid interactions. Especially, we lack a comprehensive understanding of the ways that biotic factors, including plant species richness, overall community phylogenetic and functional composition of consumers, and abiotic factors such as microclimate, determine host-parasitoid network structure and host-parasitoid community dynamics. To address this, we leveraged a 5-year dataset of trap-nesting bees and wasps and their parasitoids collected in a highly controlled, large-scale subtropical tree biodiversity experiment. We tested for effects of tree species richness, tree phylogenetic, and functional diversity, and species and phylogenetic composition on species and phylogenetic diversity of both host and parasitoid communities and the composition of their interaction networks. We show that multiple components of tree diversity and canopy cover impacted both, species and phylogenetic composition of hosts and parasitoids. Generally, phylogenetic associations between hosts and parasitoids reflected nonrandomly structured interactions between phylogenetic trees of hosts and parasitoids. Further, host-parasitoid network structure was influenced by tree species richness, tree phylogenetic diversity, and canopy cover. Our study indicates that the composition of higher trophic levels and corresponding interaction networks are determined by plant diversity and canopy cover, especially via trophic links in species-rich ecosystems.

Xylocopinae, a diverse bee subfamily comprising over 1,000 bee species, and also a major model system for studying the pollination and evolution of sociality. The lack of chromosome-level genome assembly resources for the Xylocopinae limits our research of their biology and evolution. Here, we provided the first pseudo-chromosomes genome assembly of the Xylocopa dejeanii combined PacBio CLR long reads, Illumina sequences, and Hi-C data. The final genome is 194.44 Mb located in 16 chromosomes. Our assembly includes 141 scaffolds, with a scaffold N50 length of 13.15 Mb. BUSCO analysis revealed 99.00% completeness. Genome annotation identified 28.27 Mb of repetitive elements, 10,970 protein-coding genes, and 432 ncRNAs. This high-quality X. dejeanii assembly advances our understanding of Xylocopinae genomics and provides new insights into bee evolution.