Predicting the spatial distribution of an invasive plant species (Eupatorium adenophorum) in China

By Li Zhu, O. Sun, Weiguo Sang, Zhenyu Li, K. Ma. Landscape Ecology, 2007

110 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Evolution, Behavior and Systematics, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/a6337a956ce983d13e37189332ba93efbb405e4f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Ecological Modeling

1. P. Genovesi
2. V. Jarošı́k
3. Bjørn Kløve
4. Biswajeet Pradhan
5. M. Oppenheimer
6. A. Gutierrez
7. D. Wilcove
8. Harini Nagendra

In Ecology, Evolution, Behavior and Systematics

1. Petr Pyšek
2. V. Jarošı́k
3. P. Genovesi
4. D. Wilcove
5. Yuehua Wu
6. Carlo Ricotta
7. Duccio Rocchini
8. D. Richardson

In Nature and Landscape Conservation

1. Petr Pyšek
2. V. Jarošı́k
3. P. Genovesi
4. Bjørn Kløve
5. Biswajeet Pradhan
6. M. Oppenheimer
7. D. Wilcove
8. Harini Nagendra

Citations in the corpus

• An inventory of invasive alien species in China (2012, methodology): “Using datasets on known localities invaded by A. adenophora and the environmental variables generated by the genetic algorithm for rule-set production (GARP) model, the potential future distribution of this invasive plant was modeled (Zhu et al. 2007).”
• Potential of remote sensing to predict species invasions (2015, methodology): “Despite limitations, these methods can be very useful in predicting possible locations of spread on which managers can focus their attention to limit potential future damage (Zhu et al., 2007), and in many cases they are the only readily available methods for estimating the ecological niche of a spe”
• Climate change increases risk of plant invasion in the Eastern United States (2009, methodology): “Several studies have used BEMs to model climatic habitats of invasive plant species in order to assess current invasion risk (Mau-Crimmins et al. 2006; Nielsen et al. 2008; Rouget et al. 2004; Schussman et al. 2006; Thuiller et al. 2005; Welk et al. 2002; Zhu et al. 2007 ).”
• Urban flood risk mapping using the GARP and QUEST models: A comparative study of machine learning techniques (2019, methodology): “…evolution as a presence-only modeling tool that analyzes the relationship between flood inundation dataset and topo-hydrological variables through an iterative process and conditional rules for model building (Zhu et al., 2007; Sánchez-Flores, 2007; Boeckmann and Joyner, 2014; Qin et al., 2015).”