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Abstract Shape characterizers are metrics that quantify aspects of the overall geometry of a three‐dimensional (3D) digital surface. When computed for biological objects, the values of a shape characterizer are largely independent of homology interpretations and often contain a strong ecological and functional signal. Thus, shape characterizers are useful for understanding evolutionary processes. Dirichlet normal energy (DNE) is a widely used shape characterizer in morphological studies.Recent studies found that DNE is sensitive to various procedures for preparing 3D mesh from raw scan data, raising concerns regarding comparability and objectivity when utilizing DNE in morphological research. We providearobustlyimplementedalgorithm for computing the Dirichlet energy of the normal (ariaDNE) on 3D meshes.We show through simulation that the effects of preparation‐related mesh surface attributes, such as triangle count, mesh representation, noise, smoothing and boundary triangles, are much more limited on ariaDNE than DNE. Furthermore, ariaDNE retains the potential of DNE for biological studies, illustrated by its effectiveness in differentiating species by dietary preferences.Use of ariaDNE can dramatically enhance the assessment of the ecological aspects of morphological variation by its stability under different 3D model acquisition methods and preparation procedure. Towards this goal, we provide scripts for computing ariaDNE and ariaDNE values for specimens used in previously published DNE analyses.
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popler: An r package for extraction and synthesis of population time series from the long‐term ecological research (LTER) network
Abstract Population dynamics play a central role in the historical and current development of fundamental and applied ecological science. The nascent culture of open data promises to increase the value of population dynamics studies to the field of ecology. However, synthesis of population data is constrained by the difficulty in identifying relevant datasets, by the heterogeneity of available data and by access to raw (as opposed to aggregated or derived) observations.To obviate these issues, we built a relational database,popler, and itsRclient, the library popler.popleraccommodates the vast majority of population data under a common structure, and without the need for aggregating raw observations. The popler R library is designed for users unfamiliar with the structure of the database and with the SQL language. ThisRlibrary allows users to identify, download, explore and cite datasets salient to their needs.We implemented popler as a PostgreSQL instance, where we stored population data originated by the United States Long Term Ecological Research (LTER) Network. Our focus on the US LTER data aims to leverage the potential of this vast open data resource. The database currently contains 305 datasets from 25 LTER sites.popleris designed to accommodate automatic updates of existing datasets, and to accommodate additional datasets from LTER as well as non‐LTER studies.The combination of the online database and theRlibrary popler is a resource for data synthesis efforts in population ecology. The common structure ofpoplersimplifies comparative analyses, and the availability of raw data confers flexibility in data analysis. The popler R library maximizes these opportunities by providing a user‐friendly interface to the online database.
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- Award ID(s):
- 1655499
- PAR ID:
- 10457227
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- Methods in Ecology and Evolution
- Volume:
- 11
- Issue:
- 2
- ISSN:
- 2041-210X
- Page Range / eLocation ID:
- p. 258-264
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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