Identifying geographically differentiated features of Ethopian Nile tilapia (Oreochromis niloticus) morphology with machine learning

PLoS One. 2021 Apr 15;16(4):e0249593. doi: 10.1371/journal.pone.0249593. eCollection 2021.


Visual characteristics are among the most important features for characterizing the phenotype of biological organisms. Color and geometric properties define population phenotype and allow assessing diversity and adaptation to environmental conditions. To analyze geometric properties classical morphometrics relies on biologically relevant landmarks which are manually assigned to digital images. Assigning landmarks is tedious and error prone. Predefined landmarks may in addition miss out on information which is not obvious to the human eye. The machine learning (ML) community has recently proposed new data analysis methods which by uncovering subtle features in images obtain excellent predictive accuracy. Scientific credibility demands however that results are interpretable and hence to mitigate the black-box nature of ML methods. To overcome the black-box nature of ML we apply complementary methods and investigate internal representations with saliency maps to reliably identify location specific characteristics in images of Nile tilapia populations. Analyzing fish images which were sampled from six Ethiopian lakes reveals that deep learning improves on a conventional morphometric analysis in predictive performance. A critical assessment of established saliency maps with a novel significance test reveals however that the improvement is aided by artifacts which have no biological interpretation. More interpretable results are obtained by a Bayesian approach which allows us to identify genuine Nile tilapia body features which differ in dependence of the animals habitat. We find that automatically inferred Nile tilapia body features corroborate and expand the results of a landmark based analysis that the anterior dorsum, the fish belly, the posterior dorsal region and the caudal fin show signs of adaptation to the fish habitat. We may thus conclude that Nile tilapia show habitat specific morphotypes and that a ML analysis allows inferring novel biological knowledge in a reproducible manner.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Animals
  • Bayes Theorem
  • Cichlids / anatomy & histology*
  • Ecosystem
  • Image Processing, Computer-Assisted / methods*
  • Machine Learning
  • Models, Anatomic
  • Phenotype

Grants and funding

The publication fee for this paper are provided by the Open Access Publishing Fund of the University of Natural Resources and Life Sciences, Vienna. PT and EA received funding by the Austrian Partnership Program in Higher Education and Research for Development - APPEAR, a program of the Austrian Development Cooperation which is implemented by the Austrian Agency for International Cooperation in Education and Research The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.