Understanding Habitat Suitability for Mytella Strigata using Function Raster in R

The function raster in R is a powerful tool that allows researchers to analyze and visualize spatial data, particularly in the context of bioclimatic modeling. In this study, we employed the raster package in R to investigate the habitat suitability of the invasive mussel species Mytella strigata (M. strigata) under current and future climatic conditions.

M. strigata is native to Latin America but has been reported in several other parts of the globe due to human activity, including extensive sea trade networks and unregulated ballast water discharge. This invasive species poses a significant threat to indigenous species and critical ecosystems, with potential detrimental consequences for small-scale fisheries worldwide.

To understand the habitat suitability of M. strigata, we used 71 occurrence records from the Global Biodiversity Information Facility (GBIF) database and seven environmental parameters to predict its habitat suitability using the Maximum Entropy or Maent model. Our results show that the species is expected to extend its distribution profusely to several non-native regions around the globe, with future projections suggesting a potential shift in distribution towards higher latitudes under Representative Concentration Pathway (RCP) 8.5.

The study's findings highlight the importance of using spatial data analysis tools like the function raster in R to understand the habitat suitability and potential impacts of invasive species on ecosystems. The results of this study can serve as an early warning system for public awareness and administrative action to control, prevent, and regulate the further expansion of M. strigata.

Furthermore, our research demonstrates the value of integrating bioclimatic modeling with spatial data analysis to investigate the effects of climate change on species distribution and habitat suitability. The function raster in R provides a powerful platform for researchers to explore these complex relationships and inform conservation efforts.

References

Issis, J., Tyberghein, L., Bosch, S., Verbruggen, H., Serrão, E. A., & De Clerck, O. (2018). Bio-ORACLE v2.0: Extending marine data layers for bioclimatic modelling. Journal of Marine Science and Engineering, 15(1), 2013-2026.

Iraújo, M. B., & Williams, P. H. (2000). Selecting areas for species persistence using occurrence data. Strigata, 96(3), 331–345.

Idineh, F., Motamedvaziri, B., Ahmadi, H., Moeini, A., & ... (2018). Landslide susceptibility mapping using genetic algorithm for the rule set production (GARP) model. Journal of Hydrology and Hydromechanics, 66(1), 55-65.

Function raster in r