Global distribution modelling, invasion risk assessment and niche dynamics of Leucanthemum vulgare (Ox-eye Daisy) under climate change

Abstract

In an era of climate change, biological invasions by alien species represent one of the main anthropogenic drivers of global environmental change. The present study, using an ensemble modelling approach, has mapped current and future global distribution of the invasive Leucanthemum vulgare (Ox-eye Daisy) and predicted the invasion hotspots under climate change. The current potential distribution of Ox-eye Daisy coincides well with the actual distribution records, thereby indicating robustness of our model. The model predicted a global increase in the suitable habitat for the potential invasion of this species under climate change. Oceania was shown to be the high-risk region to the potential invasion of this species under both current and future climate change scenarios. The results revealed niche conservatism for Australia and Northern America, but contrastingly a niche shift for Africa, Asia, Oceania and Southern America. The global distribution modelling and risk assessment of Ox-eye Daisy has immediate implications in mitigating its invasion impacts under climate change, as well as predicting the global invasion hotspots and developing region-specific invasion management strategies. Interestingly, the contrasting patterns of niche dynamics shown by this invasive plant species provide novel insights towards disentangling the different operative mechanisms underlying the process of biological invasions at the global scale.

Predicting the potential global distribution of Ageratina adenophora under current and future climate change scenarios

“…SDMs have been widely applied to predict the potential suitable areas of IAS based on niche conservatism, which assumes that an IAS will retain a similar niche in the native and introduced regions (Ahmad et al., 2019; Graham, 2005). Although it is still controversial whether species niches are conserved across space and time (Atwater et al., 2018), recent research supports the niche conservatism hypothesis overall (Liu et al., 2020).…”

“…Thus, spatial thinning was performed to remove the spatial autocorrelation and sampling bias. Grid cells with dimensions of 10 × 10 km were created, and a single occurrence point was selected randomly from each cell with more than one occurrence point (Ahmad et al., 2019). A total of 741 unbiased occurrence data points from regions in Asia (74 points), Africa (68 points), Australia (344 points), Oceania (70 points), North America (101 points in total and 48 points from native ranges), and South America (two points) were saved in CSV format (Figure 1).…”

“…Several recent studies have analyzed the potential changes in IAS distributions under multiple climate change scenarios at regional and global scales. Species distribution models (SDMs) have been widely applied in the early detection IAS (Ahmad et al., 2019; Padalia et al., 2014; Rodríguez‐Merino et al., 2018; Srivastava et al., 2018; Zhang et al., 2015; Zhao et al., 2013) by mapping potential IAS distribution and quantifying the relationships between IAS and environmental factors based on occurrence‐only data and species habitat conditions (e.g., climate, soil conditions, and terrain).…”

Mapping of Eucalyptus in Natura 2000 Areas Using Sentinel 2 Imagery and Artificial Neural Networks

“…We also call for future research to detect invasive species in the other Natura 2000 areas, especially the Continental and Boreal regions in the European Union. The Continental and Boreal regions are under-represented in terms of ecological research among the Natura 2000 areas [29] and furthermore these areas are experiencing an accelerated global warming [51], making them more vulnerable to biological invasions ( [52,53]). Detection of invasive species with machine learning and Sentinel 2 satellite imagery, as shown by this study, could help mitigate the negative impact of these species, by conveying results to stakeholders, such as authorities who are responsible for the management of Natura 2000 sites.…”

Identifying Invasive Weed Species in Alpine Vegetation Communities Based on Spectral Profiles

“…With the variable effectiveness of remote sensing in weed management, it is useful to perform primary studies to ascertain the potential benefits and results of implementing such weed management solutions [20,21]. This study investigates the effectiveness of spectral profiles for identifying invasive species with a focused case study of three noxious weeds, namely Ox-Eye Daisy (Leucanthemum vulgare (Asteraceae)), Orange Hawkweed (Heiracium aurantiacum) and Mouse-ear Hawkweed (Hieracium pilosella) in Kosciuszko National Park, New South Wales, Australia [22][23][24]. Determining the spectral signature through ground-based reflectance is a critical aspect of this research and has other benefits.…”