Predicting the potential distribution of Amblyomma americanum (Acari: Ixodidae) infestation in New Zealand, using maximum entropy-based ecological niche modelling

Abstract

Although currently exotic to New Zealand, the potential geographic distribution of Amblyomma americanum (L.), the lone star tick, was modelled using maximum entropy (Max-Ent). The MaxEnt model was calibrated across the native range of A. americanum in North America using present-day climatic conditions and occurrence data from museum collections. The resulting model was then projected onto New Zealand using both present-day and future climates modelled under two greenhouse gas emission scenarios, representative concentration pathways (RCP) 4.5 (low) and RCP 8.5 (high). Three sets of WorldClim bioclimatic variables were chosen using the jackknife method and tested in MaxEnt using different combinations of model feature class functions and regularization multiplier values. The preferred model was selected based on partial receiver operating characteristic tests, the omission rate and the lowest Akaike information criterion. The final model had four bioclimatic variables, Annual Mean Temperature (BIO 1), Annual Precipitation (BIO 12), Precipitation Seasonality (BIO 15) and Precipitation of Driest Quarter (BIO 17), and the projected New Zealand distribution was broadly similar to that of Haemaphysalis longicornis Neumann, New Zealand's only livestock tick, but with a more extensive predicted suitability. The climate change predictions for the year 2050 under both low and high RCP scenarios projected only moderate increases in habitat suitability along the mountain valleys in the South Island. In conclusion, this analysis shows that given the opportunity and license A. americanum could and would successfully establish in New Zealand and could provide another vector for theileriosis organisms.

Nested Species Distribution Models of Chlamydiales in Ixodes ricinus (Tick) Hosts in Switzerland

“…Our results thus highlighted the importance of considering the environment around the sampling point for a good variables estimation in species distribution model, while single point is commonly considered (36)(37)(38)(39)(40)(41)(42)(43)(44)(45). Our results also showed that the time period considered before the sampling date, with sliding windows, has a significant impact on the performance of the resulting models.…”

“…These data summarized climatic conditions from 1950 to 2000. Therefore, in these studies as in many others (36)(37)(38)(39)(40)(41)(42)(43)(44) environmental data were extracted at a resolution that did not match the species ecology and more importantly the environmental conditions at sampling dates. Our goals were thus first to build a model of higher spatial resolution (100 m) for Switzerland and second to use recent climatic data to characterize in detail the distribution of Ixodes ricinus and its associated Chlamydiales bacterial pathogen over Switzerland from 2009 to 2019.…”

Predicting <i>Tachypleus gigas</i> Spawning Distribution with Climate Change in Northeast Coast of India

“…Moreover, values (probability) from maximum entropy model were predicting impacts by ticks from land use (development) using present tick distribution and environment data (Braunisch et al, 2008). Therefore, we con- future conditions at the area of study (Remya, et al, 2015;Raghavan et al, 2020). In fact, the novelty of this study involves using maximum entropy to predict the horseshoe crab spawning grounds over a large area (c.a.…”

Spatial Suitability Modeling of Zoonosis: Implicated Risk Areas of B. anthracis and Trends under climate change Scenarios in Ethiopia

“…Thus, the ENMevaluate result of ΔAICc (delta akaikae information criterion adjusted for small sample size) which is equal to 0 was chosen. ΔAICc has been used by several authors to select the best possible features of environmental layers and to choose the corresponding Regularization multiplier prior to running MaxEnt modeling [19–20] [24].…”

“…Maximum Entropy (MaxEnt) modeling technique has long been used to model species distribution across the area of interest including disease causing agents and vectors [12][13][14] [ [19][20]. MaxEnt is the preferred method to model species distribution because of its simplicity to employ and can easily extrapolate the current distribution of species in charge into the future projectile climatic scenarios.…”