Abstract
Invasive weeds are a serious problem worldwide, threatening biodiversity and damaging economies. Modeling potential distributions of invasive weeds can prioritize locations for monitoring and control efforts, increasing management efficiency. Forecasts of invasion risk at regional to continental scales are enabled by readily available downscaled climate surfaces together with an increasing number of digitized and georeferenced species occurrence records and species distribution modeling techniques. However, predictions at a finer scale and in landscapes with less topographic variation may require predictors that capture biotic processes and local abiotic conditions. Contemporary remote sensing (RS) data can enhance predictions by providing a range of spatial environmental data products at fine scale beyond climatic variables only. In this study, we used the Global Biodiversity Information Facility (GBIF) and empirical maximum entropy (MaxEnt) models to model the potential distributions of 14 invasive plant species across Southeast Asia (SEA), selected from regional and Vietnam’s lists of priority weeds. Spatial environmental variables used to map invasion risk included bioclimatic layers and recent representations of global land cover, vegetation productivity (GPP), and soil properties developed from Earth observation data. Results showed that combining climate and RS data reduced predicted areas of suitable habitat compared with models using climate or RS data only, with no loss in model accuracy. However, contributions of RS variables were relatively limited, in part due to uncertainties in the land cover data. We strongly encourage greater adoption of quantitative remotely sensed estimates of ecosystem structure and function for habitat suitability modeling. Through comprehensive maps of overall predicted area and diversity of invasive species, we found that among lifeforms (herb, shrub, and vine), shrub species have higher potential invasion risk in SEA. Native invasive species, which are often overlooked in weed risk assessment, may be as serious a problem as non-native invasive species. Awareness of invasive weeds and their environmental impacts is still nascent in SEA and information is scarce. Freely available global spatial datasets, not least those provided by Earth observation programs, and the results of studies such as this one provide critical information that enables strategic management of environmental threats such as invasive species.
Highlights
Invasive plants have emerged as a serious problem for global biodiversity
In order to evaluate the potential distributions of selected invasive plant species in Southeast Asia (SEA) and to assess the contributions of remotely sensed environmental predictors to SDMs, we developed three model sets: models constructed along climate data only (CLIM), models with remote sensing (RS) only (RS) and models with both climate and RS data (COMB)
We modeled the potential distributions of 14 invasive species (Table 2) identified from the lists of native and non-native invasive species known in SEA (Matthews and Brand, 2004) and Vietnam (Ministry of Natural Resources and Environment and Ministry of Agriculture and Rural development, 2013)
Summary
Invasive plants have emerged as a serious problem for global biodiversity. Their infestations can lead to the extinction (Groves et al, 2003) and endangerment (Wilcove et al, 1998; Pimentel et al, 2005) of native species and the alteration of ecosystem processes (Vitousek and Walker, 1989; Simberloff, 2000). Under global climate change and human disturbance, some native species have become aggressive invasive weeds (Avril and Kelty, 1999; Wang et al, 2005; Hooftman et al, 2006; Valéry et al, 2009; Le et al, 2012). Identification of areas that are at potential invasion risk, to either non-native or native invasive species, can be an effective way to guide efficient management and prevent further incursion (Kulhanek et al, 2011)
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