Abstract
Prediction and modeling using integrated datasets and expertise from various disciplines greatly improve the management of invasive species. So far several attempts have been made to predict, handle, and mitigate invasive alien species impacts using specific efforts from various disciplines. Yet, the most persuasive approach is to better control its invasion and subsequent expansion by making use of cross-disciplinary knowledge and principles. However, the information in this regard is limited and experts from several disciplines have sometimes difficulties understanding well each other. In this respect, the focus of this review was to overview challenges and opportunities in integrating bioclimatic, remote sensing variables, and species distribution models (SDM) for predicting invasive species in data-poor regions. Google Scholar search engine was used to collect relevant papers, published between 2005–2020 (15 years), using keywords such as SDM, remote sensing of invasive species, and contribution of remote sensing in SDM, bioclimatic variables, invasive species distribution in data-poor regions, and invasive species distribution in Ethiopia. Information on the sole contribution of remote sensing and bioclimatic datasets for SDM, major challenges, and opportunities for integration of both datasets are systematically collected, analyzed, and discussed in table and figure formats. Several major challenges such as quality of remotely sensed data and its poor interpretation, inappropriate methods, poor selection of variables, and models were identified. Besides, the availability of Earth Observation (EO) data with high spatial and temporal resolution and their capacity to cover large and inaccessible areas at a reasonable cost, as well as progress in remote sensing data integration techniques and analysis are among the opportunities. Also, the impacts of important sensor characteristics such as spatial and temporal resolution are crucial for future research prospects. Similarly important are studies analyzing the impacts of interannual variability of vegetation and land use patterns on invasive SDM. Urgently needed are clearly defined working principles for the selection of variables and the most appropriate SDM.
Highlights
Invasive species are a serious worldwide threat to biodiversity (Paz-Kagan et al 2019; Somers and Asner 2012; Truong et al 2017)
Thereafter, in two separate chapters, we summarize the sole use of either climatic or Earth Observation (EO) data for species distribution models (SDM)
The chapter follows a summary of the combined use of the two datasets for SDM, followed by a review chapter on the use of such data for invasive species modeling in Ethiopia
Summary
Invasive species are a serious worldwide threat to biodiversity (Paz-Kagan et al 2019; Somers and Asner 2012; Truong et al 2017). They negatively affect livelihoods (Shackleton et al 2014, 2015), density, richness, and diversity of native woody species, and quality and distribution of water (Bekele et al 2018). They have a huge capacity to invade all land use types at high. Further aggravates the problem (Vilà et al 2011)
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