Predicting the Potential Geographic Distribution of Sirex nitobei in China under Climate Change Using Maximum Entropy Model

Abstract

Sirex nitobei, an Asian native wood wasp species, is a major pest in coniferous commercial forestry, infesting and weakening conifers through its obligate mutualism with a wood-rotting fungus species. The combination of wood wasp larvae and obligate mutualistic fungus causes the breakdown of plant vascular tissue, leading to the weakening of the plant and eventually to death, as well as a high economic cost in commercial forestry. Since it was first recorded in China in the early 1980s, S. nitobei has widely spread and become successfully established. Despite its extensive distribution range, little is known about the factors influencing current and future distribution patterns for potential pest control and monitoring. We used a maximum entropy model in conjunction with climate variables and shared socio-economic pathways to predict the current and future distribution of S. nitobei in China. We used the jackknife method and correlation analysis to select the bioclimatic and environmental variables that influence the geographic distribution of S. nitobei, which resulted in the inclusion of the monthly total precipitation in July (prec7), the monthly average maximum temperature in February (tmax2), the monthly average minimum temperature in July (tmin7), the monthly total precipitation in December (prec12), and isothermality (bio3). We found that precipitation and temperature influenced the potentially suitable areas, as predicted by the maximum entropy model. Moreover, the association of the fungus, the wood wasp, and the host plant impacts are related to availability of moisture and temperature, where moisture affects the growth of the fungus, and temperature influences the emergence, development and growth of larvae. Under the current climate conditions, the total potential suitable areas increased by 18.74%, while highly suitable and moderately suitable areas increased by 28.35 and 44.05%, respectively, under the 2081–2100 ssp245, 370 scenarios. Favorable conditions under climate change, low rainfall, and high temperature will favor the speedy larval development, the growth of its obligate nutritional fungal mutualist and the ability of S. nitobei to rapidly spread in previously unsuitable areas.

Predicting the distributions ofPouteria adolfi-friedericiandPrunus africanaiconic tree species under current and future climate change scenarios in Ethiopia

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Global potential distribution prediction of Xanthium italicum based on Maxent model

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“…The LHQPT feature classes were selected in this model and the regularization multiplier was set to 1.0 49 . The ENMeval package was used to calculate the corrected Akaike information criterion correction value (AICc value) under these parameters, and the “checkerboard2” method was used to calculate the AICc value 50 . The output was a grid layer of ASCII format, and the value of each grid in the layer represented the adaptation of X. italicum to the environment in that area, with a range of 0 to 1.…”

Pest categorisation of Sirex nitobei

“…The areas identified by Gao et al (2021b) as suitable for establishment overlap with K € oppen-Geiger climate type Cfa which occurs in the EU (Figure 9).…”