Articles

2022

1. 

   Trade-off between deep learning for species identification and inference about predator-prey co-occurrence: Reproducible R workflow integrating models in computer vision and ecological statistics

   Olivier Gimenez, Maelis Kervellec, Jean-Baptiste Fanjul, Anna Chaine, Lucile Marescot, Yoann Bollet, and Christophe Duchamp

   Computo, 2022.

   Abs HTML git repo Review Bib

   Deep learning is used in computer vision problems with important applications in several scientific fields. In ecology for example, there is a growing interest in deep learning for automatizing repetitive analyses on large amounts of images, such as animal species identification. However, there are challenging issues toward the wide adoption of deep learning by the community of ecologists. First, there is a programming barrier as most algorithms are written in Python while most ecologists are versed in R. Second, recent applications of deep learning in ecology have focused on computational aspects and simple tasks without addressing the underlying ecological questions or carrying out the statistical data analysis to answer these questions. Here, we showcase a reproducible R workflow integrating both deep learning and statistical models using predator-prey relationships as a case study. We illustrate deep learning for the identification of animal species on images collected with camera traps, and quantify spatial co-occurrence using multispecies occupancy models. Despite average model classification performances, ecological inference was similar whether we analysed the ground truth dataset or the classified dataset. This result calls for further work on the trade-offs between time and resources allocated to train models with deep learning and our ability to properly address key ecological questions with biodiversity monitoring. We hope that our reproducible workflow will be useful to ecologists and applied statisticians.

   computer vision, deep-learning, species distribution modeling, ecological statistics

   @article{gimenez_lynx,
     bibtex_show = {true},
     author = {Gimenez, Olivier and Kervellec, Maelis and Fanjul, Jean-Baptiste and Chaine, Anna and Marescot, Lucile and Bollet, Yoann and Duchamp, Christophe},
     title = {{Trade-off between deep learning for species
                       identification and inference about predator-prey
                       co-occurrence: Reproducible R workflow integrating
                       models in computer vision and ecological
                       statistics}},
     journal = {Computo},
     year = {2022},
     doi = {10.57750/yfm2-5f45},
     html = {https://computo.sfds.asso.fr/published-202204-deeplearning-occupancy-lynx/},
     review = {https://github.com/computorg/published-202204-deeplearning-occupancy-lynx/issues?q=is%3Aopen+is%3Aissue+label%3Areview},
     code = {https://github.com/computorg/published-202204-deeplearning-occupancy-lynx/},
     type = {{Research article}},
     language = {R},
     domain = {Statistical Ecology},
     keywords = {computer vision, deep-learning, species distribution modeling, ecological statistics},
     issn = {2824-7795}
   }

1. 

   Visualizing Data using t-SNE: practical Computo example

   Laurens Maaten, and Geoffrey Hinton

   Computo, 2021.

   Abs HTML git repo Data Review Bib

   We present a new technique called “t-SNE” that visualizes high-dimensional data by giving each datapoint a location in a two or three-dimensional map. The technique is a variation of Stochastic Neighbor Embedding hinton:stochastic that is much easier to optimize, and produces significantly better visualizations by reducing the tendency to crowd points together in the center of the map. t-SNE is better than existing techniques at creating a single map that reveals structure at many different scales. This is particularly important for high-dimensional data that lie on several different, but related, low-dimensional manifolds, such as images of objects from multiple classes seen from multiple viewpoints. For visualizing the structure of very large data sets, we show how t-SNE can use random walks on neighborhood graphs to allow the implicit structure of all the data to influence the way in which a subset of the data is displayed. We illustrate the performance of t-SNE on a wide variety of data sets and compare it with many other non-parametric visualization techniques, including Sammon mapping, Isomap, and Locally Linear Embedding. The visualization produced by t-SNE are significantly better than those produced by other techniques on almost all of the data sets.

   template, documentation, quarto, R, python

   @article{mock_tsne,
     bibtex_show = {true},
     author = {van der Maaten, Laurens and Hinton, Geoffrey},
     title = {{Visualizing Data using t-SNE: practical Computo example}},
     journal = {Computo},
     year = {2021},
     volume = {0},
     doi = {},
     html = {https://computorg.github.io/published-paper-tsne},
     code = {https://github.com/computorg/published-paper-tsne},
     data = {},
     review = {https://github.com/computorg/published-paper-tsne/issues},
     type = {Template},
     language = {R, Python},
     domain = {Template},
     keywords = {template, documentation, quarto, R, python}
   }