Biological control opportunities of ragweed are predicted to decrease with climate change in East Asia

doi:10.17520/biods.2017096

Abstract

Abstract:

The control of invasive alien plants (IAP) that jeopardize our ecosystems and economy constitutes a significant challenge for natural resource management. Classical biological control referring to the introduction of specialist antagonists from the native range has proven to be a highly cost-effective management tool against IAP. A critical issue in biological control research is to guide informed decision-making on the potential spread and distribution and thus impact of biological control candidates, especially under climate change. Here we propose a biogeographic modeling approach to predict the cover of the suitable area of a plant invader in East Asia (EA) by two biological control agents and their combinations. Our study system is Ambrosia artemisiifolia, native to North America and invasive worldwide, and two North American biological control agents, Ophraella communa and Epiblema strenuana that were accidentally and deliberately introduced into East Asia (EA) in the late 20th century, respectively. Specifically, we ask: (1) what percentage of the suitable A. artemisiifolia area is also suitable for the two agents in EA, and (2) which part of the suitable A. artemisiifolia area in EA is likely to remain uncovered by these two agents, both under current and future climatic scenarios; and (3) which particular biotypes would be needed to fill in the yet uncovered part of the suitable A. artemisiifolia range in East Asia? For this, we simultaneously modelled the species distributions based on worldwide occurrences and important bioclimatic variables for the target invasive plant and its two biological control agents. Ordination techniques were used to explore climatic constraints of each species and to perform niche overlap and similarity tests with A. artemisiifolia between its native North American and introduced EA range. Our results show that O. communa has a larger overlap with the geographic range of A. artemisiifolia than E. strenuana, both under current (40.3% vs. 21.6% for O. communa and E. strenuana, respectively) and future climatic scenarios (29.8% vs. 20.3% for O. communa and E. strenuana, respectively). Importantly, climate change is expected to reduce the total geographic overlap of A. artemisiifolia by the two agents combined (42.9% vs. 29.8% for current and future climate conditions, respectively), with a higher reduction by O. communa than by E. strenuana. Our analyses also identified for which abiotic conditions to select in order to develop climatically adapted strains for particular regions, where A. artemisiifolia is presently unlikely to be covered.

http://www.biodiversity-science.net/fileup/PDF/w2017-096-en.pdf

Key words: biological control, biological invasions, Epiblema strenuana, niche overlap, Ophraella communa, species distribution

Yan Sun, Zhongshi Zhou, Rui Wang, Heinz Müller-Schärer. Biological control opportunities of ragweed are predicted to decrease with climate change in East Asia[J]. Biodiv Sci, 2017, 25(12): 1285-1294.

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URL: https://www.biodiversity-science.net/EN/10.17520/biods.2017096

https://www.biodiversity-science.net/EN/Y2017/V25/I12/1285

Figures/Tables 3

Table 1 AUC power of all species using four models under current and future climate scenarios showing acceptable AUC scores

	Ambrosia artemisiifolia	Ophraella communa	Epiblema strenuana
当前气候背景 Current climate scenario
一般线性模型 GLM	0.88±0.002	0.88±0.004	0.84±0.01
广义助推模型 GBM	0.89±0.002	0.90±0.003	0.89±0.006
随机森林模型 RF	0.89±0.003	0.91±0.003	0.89±0.006
最大熵模型MaxEnt	0.87±0.003	0.83±0.004	0.83±0.009
未来气候背景: HD-26 Future climate scenario: HD-26
一般线性模型 GLM	0.87±0.003	0.87±0.002	0.83±0.01
广义助推模型 GBM	0.89±0.002	0.90±0.002	0.90±0.006
随机森林模型 RF	0.90±0.002	0.91±0.002	0.91±0.006
最大熵模型MaxEnt	0.86±0.003	0.83±0.003	0.80±0.01
未来气候背景: HD-85 Future climate scenario: HD-85
一般线性模型 GLM	0.88±0.003	0.88±0.002	0.84±0.008
广义助推模型 GBM	0.90±0.003	0.92±0.001	0.86±0.008
随机森林模型 RF	0.89±0.003	0.92±0.001	0.86±0.009
最大熵模型MaxEnt	0.88±0.003	0.84±0.003	0.78±0.01
未来气候背景: IP-26 Future climate scenario: IP-26
一般线性模型 GLM	0.89±0.003	0.87±0.002	0.87±0.01
广义助推模型 GBM	0.90±0.002	0.90±0.002	0.91±0.008
随机森林模型 RF	0.91±0.002	0.91±0.001	0.91±0.008
最大熵模型MaxEnt	0.89±0.003	0.85±0.002	0.82±0.009
未来气候背景: IP-85 Future climate scenario: IP-85
一般线性模型 GLM	0.87±0.003	0.87±0.002	0.88±0.008
广义助推模型 GBM	0.89±0.003	0.90±0.002	0.91±0.006
随机森林模型 RF	0.91±0.003	0.91±0.002	0.91±0.006
最大熵模型MaxEnt	0.87±0.003	0.84±0.003	0.88±0.008

Fig. 1 Geographical predictions of Ambrosia artemisiifolia and two biological control insects for East Asia, under present and future climatic scenarios. The climatic suitability indicates the optimal threshold of the percentage of models predicting each species. Dark green in all figures, A. artemisiifolia; under current climatic conditions: (A) Red, Ophraella communa; sienna, overlap 40.3%; (B) Blue, Epiblema strenuana; sienna, overlap 21.6%; under future climatic scenarios: (C) Red, Ophraella communa; sienna, overlap 29.8%; (D) Blue, Epiblema strenuana; sienna, overlap 20.3%. Models calibrated in East Asia only.

Fig. 2 Niche of Ambrosia artemisiifolia in climatic space using principal component analysis (PCA-env). Panels (A) and (B) represent the niche of the species along the two first axes of the PCA for the native North American (NA) and introduced East Asian (EA) range, respectively. Gray shading shows the density of the occurrences of the species by the cell. The solid contour lines illustrate 100% of the available environment, and dashed lines indicate the 50% of the most common background environment. Yellow circles in (A) and (B) give the occurrences of two insect species in NA and in EA. The contribution of the climatic variables of the two axes of the PCA and the percentage of inertia explained by the two axes is given in (C). Histograms (D-F) show the observed niche overlap between the two ranges (bars and a diamond) and simulated niche overlaps (gray bars) on which tests of niche equivalency (D), niche similarity of EA and NA (E), and niche similarity of NA and EA (F) are calculated from 100 iterations, with the significance level of the tests.

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