scholarly journals Integrating transport pressure data and species distribution models to estimate invasion risk for alien stowaways

Ecography ◽  
2017 ◽  
Vol 41 (4) ◽  
pp. 635-646 ◽  
Author(s):  
Reid Tingley ◽  
Pablo García-Díaz ◽  
Carla Rani Rocha Arantes ◽  
Phillip Cassey
Keyword(s):  
Species Distribution ◽  
Species Distribution Models ◽  
Pressure Data ◽  
Invasion Risk ◽  
Distribution Models

Bioenergy and Invasive Plants: Quantifying and Mitigating Future Risks

2014 ◽  
Vol 7 (2) ◽  
pp. 199-209 ◽  
Author(s):  
Jacob N. Barney
Keyword(s):  
United States ◽  
Risk Assessment ◽  
Species Distribution ◽  
Species Distribution Models ◽  
Empirical Studies ◽  
The United States ◽  
State Regulation ◽  
Bioenergy Crops ◽  
Invasion Risk ◽  
Distribution Models

AbstractThe United States is charging toward the largest expansion of agriculture in 10,000 years with vast acreages of primarily exotic perennial grasses planted for bioenergy that possess many traits that may confer invasiveness. Cautious integration of these crops into the bioeconomy must be accompanied by development of best management practices and regulation to mitigate the risk of invasion posed by this emerging industry. Here I review the current status of United States policy drivers for bioenergy, the status of federal and state regulation related to invasion mitigation, and survey the scant quantitative literature attempting to quantify the invasive potential of bioenergy crops. A wealth of weed risk assessments are available on exotic bioenergy crops, and generally show a high risk of invasion, but should only be a first-step in quantifying the risk of invasion. The most information exists for sterile giant miscanthus, with preliminary empirical studies and demographic models suggesting a relatively low risk of invasion. However, most important bioenergy crops are poorly studied in the context of invasion risk, which is not simply confined to the production field; but also occurs in crop selection, harvest and transport, and feedstock storage. Thus, I propose a nested-feedback risk assessment (NFRA) that considers the entire bioenergy supply chain and includes the broad components of weed risk assessment, species distribution models, and quantitative empirical studies. New information from the NFRA is continuously fed back into other components to further refine the risk assessment; for example, empirical dispersal kernels are utilized in landscape-level species distribution models, which inform habitat invasibility studies. Importantly, the NFRA results in a relative invasion risk to known species (e.g., is giant reed a higher or lower invasion risk than johnsongrass). This information is used to design robust mitigation plans that include record keeping, regular scouting and reporting, prudent harvest and transport practices that consider species biology, and eradication protocols as an ultimate precaution. Finally, a socio-political balance must be struck (i.e., a cost-benefit analysis) among our energy choices that consider the broader implications, which includes the risk of future invasions.

scholarly journals An invasion risk map for non-native aquatic macrophytes of the Iberian Peninsula

2017 ◽  
Vol 74 (1) ◽  
pp. 055 ◽  
Author(s):  
Argantonio Rodríguez-Merino ◽  
Rocío Fernández-Zamudio ◽  
Pablo García-Murillo
Keyword(s):  
Iberian Peninsula ◽  
Species Distribution ◽  
Native Species ◽  
Aquatic Macrophytes ◽  
Species Distribution Models ◽  
Green Revolution ◽  
High Sensitivity ◽  
Risk Map ◽  
Invasion Risk ◽  
Distribution Models

Freshwater systems are particularly susceptible to non-native organisms, owing to their high sensitivity to the impacts that are caused by these organisms. Species distribution models, which are based on both environmental and socio-economic variables, facilitate the identification of the most vulnerable areas for the spread of non-native species. We used MaxEnt to predict the potential distribution of 20 non-native aquatic macrophytes in the Iberian Peninsula. Some selected variables, such as the temperature seasonality and the precipitation in the driest quarter, highlight the importance of the climate on their distribution. Notably, the human influence in the territory appears as a key variable in the distribution of studied species. The model discriminated between favorable and unfavorable areas with high accuracy. We used the model to build an invasion risk map of aquatic macrophytes for the Iberian Peninsula that included results from 20 individual models. It showed that the most vulnerable areas are located near to the sea, the major rivers basins, and the high population density areas. These facts suggest the importance of the human impact on the colonization and distribution of non-native aquatic macrophytes in the Iberian Peninsula, and more precisely agricultural development during the Green Revolution at the end of the 70’s. Our work also emphasizes the utility of species distribution models for the prevention and management of biological invasions.

scholarly journals A Robust Prediction Model for Species Distribution Using Bagging Ensembles with Deep Neural Networks

2021 ◽  
Vol 13 (8) ◽  
pp. 1495
Author(s):  
Jehyeok Rew ◽  
Yongjang Cho ◽  
Eenjun Hwang
Keyword(s):  
Neural Networks ◽  
Species Distribution ◽  
Best Practice ◽  
Deep Neural Networks ◽  
Species Distribution Models ◽  
Species Distribution Model ◽  
Environmental Data ◽  
Distribution Model ◽  
Distribution Models ◽  
Robust Prediction

Species distribution models have been used for various purposes, such as conserving species, discovering potential habitats, and obtaining evolutionary insights by predicting species occurrence. Many statistical and machine-learning-based approaches have been proposed to construct effective species distribution models, but with limited success due to spatial biases in presences and imbalanced presence-absences. We propose a novel species distribution model to address these problems based on bootstrap aggregating (bagging) ensembles of deep neural networks (DNNs). We first generate bootstraps considering presence-absence data on spatial balance to alleviate the bias problem. Then we construct DNNs using environmental data from presence and absence locations, and finally combine these into an ensemble model using three voting methods to improve prediction accuracy. Extensive experiments verified the proposed model’s effectiveness for species in South Korea using crowdsourced observations that have spatial biases. The proposed model achieved more accurate and robust prediction results than the current best practice models.

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