Spatial Downscaling of Alien Species Presences Using Machine Learning

Abstract

Spatially explicit assessments of alien species environmental and socio-economic impacts, and subsequent management interventions for their mitigation, require large scale, high-resolution data on species presence distribution. However, these data are often unavailable. This paper presents a method that relies on Random Forest (RF) models to distribute alien species presence counts at a finer resolution grid, thus achieving spatial downscaling. A bootstrapping scheme is designed to account for sub-setting uncertainty, and subsets are used to train a sufficiently large number of RF models. RF results are processed to estimate variable importance and model performance. The method is tested with an ∼8 × 8 km 2 grid containing floral alien species presence and several potentially exploratory indices of climatic, habitat, land use, and soil property covariates for the Mediterranean island of Crete, Greece. Alien species presence is aggregated at 16 × 16 km 2 and used as a predictor of presence at the original resolution, thus simulating spatial downscaling. Uncertainty assessment of the spatial downscaling of alien species' occurrences was also performed and true/false presences and absences were quantified. The approach is promising for downscaling alien species datasets of larger spatial scale but coarse resolution, where the underlying environmental information is available at a finer resolution. Furthermore, the RF architecture allows for tuning toward operationally optimal sensitivity and specificity, thus providing a decision support tool for designing a resource efficient alien species census.

Seamounts as hot-spots of large pelagic aggregations

“…Based on these values, the Matthews correlation coefficient (MCC; Matthews, 1975), a reduction of the Pearson correlation coefficient for binary variables (Baldi and Brunak, 2001), is considered a solid criterion of machine learning performance (Bhasin and Raghava, 2004;Chen et al, 2004;Bao and Cui, 2005;Daliakopoulos et al, 2017). MCC is particularly useful for imbalanced datasets where the disparity between the numbers of presence and absence samples is significant.…”

Robinia pseudoacacia L. in Short Rotation Coppice: Seed and Stump Shoot Reproduction as well as UAS-based Spreading Analysis

“…In addition, machine learning algorithms are increasingly being used for data and image analysis [52,62,[72][73][74][75][76][77][78][79]. The CNN applied in the current study was tested to classify single black locust images under varying conditions and attained a high test accuracy of 99.5%.…”

“…Furthermore, there is an increasing interest in machine learning algorithms for data and image analysis, such as the application of the random forest model [52,[72][73][74][75][76], support vector machine [73][74][75][76], and deep learning algorithms, especially convolutional neural networks (CNNs) [62,73,75,[77][78][79]. However, CNNs were not previously utilized for the classification of black locust in short rotation coppices under varying conditions in single images.…”

Commentary: Aedes albopictus and Aedes japonicus—two invasive mosquito species with different temperature niches in Europe

“…For example, in Zhang and Zhang (2012) the authors propose an effective ENAP to assess the impacts of predictor variables and ASDM. In Daliakopoulos et al (2017) the Random Forest EANP has proven that it can provide a better understanding of facilitating and limiting factors of alien species presence, both for research and management purposes. Finally, Lauzeral et al (2012) proposes an iterative ENAP to ensure noise absence and hence to improve the predictive reliability of ensemble modeling of species distributions.…”