Biodiversity studies rely heavily on estimates of species' distributions often obtained through ecological niche modelling. Numerous software packages exist that allow users to model ecological niches using machine learning and statistical methods. However, no existing package with a graphical user interface allows users to perform model calibration and selection based on convex forms such as ellipsoids, which may match fundamental ecological niche shapes better, incorporating tools for exploring, modelling, and evaluating niches and distributions that are intuitive for both novice and proficient users. Here we describe an The method is explained in detail and tested via modelling the threatened feline species Using
Pressing environmental research questions demand the integration of increasingly diverse and large‐scale ecological datasets as well as complex analytical methods, which require specialized tools and resources. Computational training for ecological and evolutionary sciences has become more abundant and accessible over the past decade, but tool development has outpaced the availability of specialized training. Most training for scripted analyses focuses on individual analysis steps in one script rather than creating a scripted pipeline, where modular functions comprise an ecosystem of interdependent steps. Although current computational training creates an excellent starting place, linear styles of scripting can risk becoming labor‐ and time‐intensive and less reproducible by often requiring manual execution. Pipelines, however, can be easily automated or tracked by software to increase efficiency and reduce potential errors. Ecology and evolution would benefit from techniques that reduce these risks by managing analytical pipelines in a modular, readily parallelizable format with clear documentation of dependencies. Workflow management software (WMS) can aid in the reproducibility, intelligibility and computational efficiency of complex pipelines. To date, WMS adoption in ecology and evolutionary research has been slow. We discuss the benefits and challenges of implementing WMS and illustrate its use through a case study with the Although WMS requires familiarity with function‐oriented programming and careful planning for more advanced applications and pipeline sharing, investment in training will enable access to the benefits of WMS and impart transferable computing skills that can facilitate ecological and evolutionary data science at large scales.
- NSF-PAR ID:
- 10419689
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- Methods in Ecology and Evolution
- Volume:
- 14
- Issue:
- 6
- ISSN:
- 2041-210X
- Format(s):
- Medium: X Size: p. 1381-1388
- Size(s):
- ["p. 1381-1388"]
- Sponsoring Org:
- National Science Foundation
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