EcoCommons & Open EcoAcoustics SDM use case

  1. Examples of code and the associated text summaries describe how open ecoacoustics https://openecoacoustics.org/ data can generate better SDM predictions. By using long-term monitoring data from https://acousticobservatory.org/ which allows analysts to infer absence locations, which does a much better job at predicting distributions than presence only methods, and which facilitate use of call frequency as a response variable rather than presence absence.

The code and data used to generate these examples:
https://github.com/andrew-1234/sdm-usecase-master

  1. Shows one way to overlay areas with the least geographically and environmentally representative sampling in addition to the predicted probability of occurrence generated by an SDM. This shows how to spatially represent areas where additional acoustic sampling would increase representative sampling most.

The code used in this example:
https://github.com/EcoCommons-Australia/educational_material/tree/main/SDMs_in_R/Scripts/adding_uncertainty_to_the_map

Licence: Creative Commons Attribution 4.0

Contact: https://www.ecocommons.org.au/contact/

Keywords: Species Distribution Modelling, Ecoacoustics, Ecology, Owls, Mapping uncertainty


Additional information

Target audience: Undergraduate, Masters student, MBR student, PhD student

Status: Active

EcoCommons & Open EcoAcoustics SDM use case https://dresa.org.au/materials/ecocommons-open-ecoacoustics-sdm-use-case 1. Examples of code and the associated text summaries describe how open ecoacoustics https://openecoacoustics.org/ data can generate better SDM predictions. By using long-term monitoring data from https://acousticobservatory.org/ which allows analysts to infer absence locations, which does a much better job at predicting distributions than presence only methods, and which facilitate use of call frequency as a response variable rather than presence absence. The code and data used to generate these examples: https://github.com/andrew-1234/sdm-usecase-master 2. Shows one way to overlay areas with the least geographically and environmentally representative sampling in addition to the predicted probability of occurrence generated by an SDM. This shows how to spatially represent areas where additional acoustic sampling would increase representative sampling most. The code used in this example: https://github.com/EcoCommons-Australia/educational_material/tree/main/SDMs_in_R/Scripts/adding_uncertainty_to_the_map https://www.ecocommons.org.au/contact/ Species Distribution Modelling, Ecoacoustics, Ecology, Owls, Mapping uncertainty ugrad masters mbr phd