Ecological niche shift and suitable area expansion of a globally invasive species Phthorimaea operculella.

Phthorimaea operculella is a major potato pest of global importance, early warning and detection of which are of significance. In this study, we analyzed the climate niche conservation of P. operculella during its invasion by comparing the overall climate niche from three dimensions, including the differences between native range (South America) and entire invaded region (excluding South America), the differences bwtween native range (South America) and five invaded continents (North America, Oceania, Asia, Africa, and Europe), as well as the differences between native region (South America) and an invaded region (China). We constructed ecological niche models for its native range (South America) and invaded region (China). The results showed that the climatic niche of the pest has expanded to varying degrees in different regions, indicating that the pest could well adapt to new environments during the invasion. Almost all areas of South America are suitable for P. operculella. In China, its suitable area is mainly concentrated in Shandong, Hebei, Tianjin, Beijing, Henan, Hubei, Yunnan, Guizhou, Sichuan, Hainan, northern Guangxi, southern Hunan, Anhui, Guangdong, Jiangsu, southern Shanxi, and southern Shaanxi. With increasing greenhouse gas emissions and global temperature, its suitable area will decrease at low latitude and increase gradually at high latitude. Specifically, the northern boundary will extend to Liaoning, Jilin, and the southeastern region of Inner Mongolia, while the western boundary extends to Sichuan and the southeast Qinghai-Tibet Plateau. The suitable area in the southeast Yunnan-Guizhou Plateau, Hainan Island, and the south of Yangtze River, will gradually decrease. The total suitable habitat area for P. operculella in China is projected to increase under future climate condition. From 2081 to 2100, under the three greenhouse gas emissions scenarios of ssp126, ssp370, and ssp585, the suitable area is expected to increase by 27.78, 165.54, and 140.41 hm2, respectively. Therefore, it is crucial to strengtehen vigilance and implement strict measures to prevent the further expansion of P. operculella.

Identification of Species by Combining Molecular and Morphological Data Using Convolutional Neural Networks.

Integrative taxonomy is central to modern taxonomy and systematic biology, including behavior, niche preference, distribution, morphological analysis, and DNA barcoding. However, decades of use demonstrate that these methods can face challenges when used in isolation, for instance, potential misidentifications due to phenotypic plasticity for morphological methods, and incorrect identifications because of introgression, incomplete lineage sorting, and horizontal gene transfer for DNA barcoding. Although researchers have advocated the use of integrative taxonomy, few detailed algorithms have been proposed. Here, we develop a convolutional neural network method (morphology-molecule network [MMNet]) that integrates morphological and molecular data for species identification. The newly proposed method (MMNet) worked better than four currently available alternative methods when tested with 10 independent data sets representing varying genetic diversity from different taxa. High accuracies were achieved for all groups, including beetles (98.1% of 123 species), butterflies (98.8% of 24 species), fishes (96.3% of 214 species), and moths (96.4% of 150 total species). Further, MMNet demonstrated a high degree of accuracy ($>$98%) in four data sets including closely related species from the same genus. The average accuracy of two modest subgenomic (single nucleotide polymorphism) data sets, comprising eight putative subspecies respectively, is 90%. Additional tests show that the success rate of species identification under this method most strongly depends on the amount of training data, and is robust to sequence length and image size. Analyses on the contribution of different data types (image vs. gene) indicate that both morphological and genetic data are important to the model, and that genetic data contribute slightly more. The approaches developed here serve as a foundation for the future integration of multimodal information for integrative taxonomy, such as image, audio, video, 3D scanning, and biosensor data, to characterize organisms more comprehensively as a basis for improved investigation, monitoring, and conservation of biodiversity. [Convolutional neural network; deep learning; integrative taxonomy; single nucleotide polymorphism; species identification.].

FuzzyID2: A software package for large data set species identification via barcoding and metabarcoding using hidden Markov models and fuzzy set methods.

Species identification through DNA barcoding or metabarcoding has become a key approach for biodiversity evaluation and ecological studies. However, the rapid accumulation of barcoding data has created some difficulties: for instance, global enquiries to a large reference library can take a very long time. We here devise a two-step searching strategy to speed identification procedures of such queries. This firstly uses a Hidden Markov Model (HMM) algorithm to narrow the searching scope to genus level and then determines the corresponding species using minimum genetic distance. Moreover, using a fuzzy membership function, our approach also estimates the credibility of assignment results for each query. To perform this task, we developed a new software pipeline, FuzzyID2, using Python and C++. Performance of the new method was assessed using eight empirical data sets ranging from 70 to 234,535 barcodes. Five data sets (four animal, one plant) deployed the conventional barcode approach, one used metabarcodes, and two were eDNA-based. The results showed mean accuracies of generic and species identification of 98.60% (with a minimum of 95.00% and a maximum of 100.00%) and 94.17% (with a range of 84.40%-100.00%), respectively. Tests with simulated NGS sequences based on realistic eDNA and metabarcode data demonstrated that FuzzyID2 achieved a significantly higher identification success rate than the commonly used Blast method, and the TIPP method tends to find many fewer species than either FuzztID2 or Blast. Furthermore, data sets with tens of thousands of barcodes need only a few seconds for each query assignment using FuzzyID2. Our approach provides an efficient and accurate species identification protocol for biodiversity-related projects with large DNA sequence data sets.