  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.
  All material (source of the Rmarkdown notebook and auxiliary files) is
available from
https://github.com/oliviergimenez/computo-deeplearning-occupany-lynx.
