Assessing the Potential Distribution of Asian Gypsy Moth in Canada: A Comparison of Two Methodological Approaches

Gypsy moth (Lymantria dispar L.) is one of the world’s worst hardwood defoliating invasive alien species. It is currently spreading across North America, damaging forest ecosystems and posing a significant economic threat. Two subspecies L. d. asiatica and L. d. japonica, collectively referred to as Asian gypsy moth (AGM) are of special concern as they have traits that make them better invaders than their European counterpart (e.g. flight capability of females). We assessed the potential distribution of AGM in Canada using two presence-only species distribution models, Maximum Entropy (MaxEnt) and Genetic Algorithm for Rule-set Prediction (GARP). In addition, we mapped AGM potential future distribution under two climate change scenarios (A1B and A2) while implementing dispersal constraints using the cellular automation model MigClim. MaxEnt had higher AUC, pAUC and sensitivity scores (0.82/1.40/1.00) when compared to GARP (0.70/1.26/0.9), indicating better discrimination of suitable versus unsuitable areas for AGM. The models indicated that suitable conditions for AGM were present in the provinces of British Columbia, Ontario, Quebec, Nova Scotia and New Brunswick. The human influence index was the variable found to contribute the most in predicting the distribution of AGM. These model results can be used to identify areas at risk for this pest, to inform strategic and tactical pest management decisions.

From a Flapping Bumblebee to an Evolving Sand Dune: A Reconstruction-Based Algorithm to Feature the Digitally Recorded Objects

A reconstruction-based image processing algorithm is developed to automatically extract feature points of digitalized 2D objects. This algorithm, which is introduced using a bumblebee flight case, is made up of two parts: a four-connected dot chasing rearrangement scheme and an extreme point extraction on a polarized contour. It is then applied to a dune evolution case that is simulated with a cellular automation model. The results show that the proposed algorithm is effective in characterizing individual moving objects. An additional algorithm is developed to categorize the extracted feature points of a bumblebee with translucent wings.

Effect of Water Flows on Ship Traffic in Narrow Water Channels Based on Cellular Automata

In narrow water channels, ship traffic may be affected by water flows and ship interactions. Studying their effects can help maritime authorities to establish appropriate management strategies. In this study, a two-lane cellular automation model is proposed. Further, the behavior of ship traffic is analyzed by setting different water flow velocities and considering ship interactions. Numerical experiment results show that the ship traffic density-flux relation is significantly different from the results obtained by classical models. Furthermore, due to ship interactions, the ship lane-change rate is influenced by the water flow to a certain degree.