Our knowledge of how niche dynamic patterns respond to invasion trajectories and influence invasion risk prediction is elusive for the majority of notorious invaders, hindering scientific understanding, biosecurity planning and practice, and management implementation. We used Mikania micrantha, one of the most notorious invasive alien species in the world, to test the hypothesis that multiple invasion trajectories could induce niche dynamics inconsistency and increase risk uncertainty of invasive alien species. We compiled a robust database of M. micrantha occurrence across its native range in Central and South America and invaded ranges in China. This database was used to clarify different invaded ranges and invasion trajectories of M. micrantha in China. Principle Component Analysis of climatic variables associated with the database was used to detect its niche dynamic patterns associated with multiple invasion trajectories. Maximum Entropy algorithm was used to predict the high-risk area of M. micrantha invasion using occurrence datasets for invaded ranges where niches remained conservative, and to identify area changes with the inclusion of occurrences datasets for invaded ranges where niche shifts occurred. M. micrantha invasion occurred in three geographically distinct regions, with conservative climate niches in southern and southeastern China and climatic niche shifts in southwestern China. A high-risk area for M. micrantha invasion spanned multiple provinces and cities, and expanded considerably with the inclusion of the occurrence dataset for southwestern China. Our findings contribute to the theoretical understanding of invasion mechanisms and the practical optimization of biosecurity planning and implementation.
Our knowledge of how niche dynamic patterns respond to invasion trajectories and influence invasion risk prediction is elusive for the majority of notorious invaders, hindering scientific understanding, biosecurity planning and practice, and management implementation of biological invasions. We used Mikania micrantha, one of the world's worst invasive alien species (IAS), to test the hypothesis that multiple invasion trajectories could induce niche dynamics inconsistency and increase risk uncertainty of IAS. We compiled a robust database of M. micrantha occurrence across its native range in Central and South America and invaded range in China. This database was used to clarify different invaded ranges and invasion trajectories of M. micrantha in China. Principal components analysis of climatic variables associated with the database was used to explore the niche dynamic patterns associated with multiple invasion trajectories of M. micrantha. Maximum entropy algorithm was used to predict the high-risk area of M. micrantha invasion using occurrence datasets for invaded ranges where niches remained conservative, and to detect area changes with the inclusion of occurrence datasets for invaded ranges where niche shifts occurred. M. micrantha invasion occurred in three geographically distinct regions, with conservative climate niches in southern and southeastern China and climatic niche shifts in southwestern China.A high-risk area for M. micrantha invasion spanned multiple provinces and cities and expanded considerably with the inclusion of the occurrence dataset for southwestern China. Our findings contribute to the theoretical understanding of invasion mechanisms and the practical optimization of biosecurity planning and implementation.
Management of biological invasions is a tremendous challenge for both ecological and socioeconomic systems. Public education positively affects stakeholder knowledge and facilitate management of invasive alien species (IAS). However, our knowledge of the role of social media in public knowledge of IAS is elusive for the majority of invaders. In this study, we used Spodoptera frugiperda (Lepidoptera; noctuidae), one of the most notorious invasive insects in China, as a case to test the hypothesis that social media could improve farmers’ knowledge of IAS. We conducted household questionnaires at the village level in the China-Myanmar-Laos border region (China) and used quantitative descriptions and binary logistic regressions in statistical analyses. Our results indicated that farmers often used 12 social media applications on smartphones (SMASs) and obtained information about S. frugiperda from six SMASs, with high preferences for WeChat and TikTok, and obtained a high level of knowledge of S. frugiperda from SMASs; further, socio-demographic factors and of SMAS-based information significantly influenced farmers’ knowledge of S. frugiperda. We suggested that well-designed and conducted educational programs based on the use of SMASs could improve the performance of IAS management, and the government plays an important role in such a circumstance. Our findings contribute to theoretical insights into the role of public education in IAS improvement, and empirical bases for the optimization of IAS management.
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