Every year, millions of birds migrate between breeding and nonbreeding habitat, but the relative numbers of animals moving between sites are difficult to observe directly. Here we propose FlywayNet, a discrete network model based on observed count data, to determine the most likely migration links between regions using statistical modelling and efficient inference tools. Our approach advances on previous studies by accounting for noisy observations and flexible stopover durations by modelling using interacting hidden semi‐Markov Models. In FlywayNet, individual birds sojourn in stopover nodes for a period of time before moving to other nodes with an unknown probability that we aim to estimate. Exact estimation using existing approaches is not possible, so we designed customised versions of the Monte Carlo expectation‐maximisation and approximate Bayesian computation algorithms for our model. We compare the efficiency and quality of estimation of these approaches on synthetic data and an applied case study. Our algorithms performed well on benchmark problems, with low absolute error and strong correlation between estimated and known parameters. On a case study using citizen science count data of the Far Eastern Curlew (Numenius madagascariensis), an endangered shorebird from the East Asian–Australasian Flyway, the ABC and MCEM algorithms generated contrasting recommendations due to a difference in optimisation criteria and noise in the data. For ABC, we recovered key features of population‐level movements predicted by experts despite the challenges of noisy unstructured data. Understanding connectivity places local conservation efforts and threat mitigation in the global context, yet it has proven difficult to rigorously quantify connectivity at the population level. Our approach provides a flexible framework to infer the structure of migratory networks in birds and other organisms.

An accurate and complete taxonomic description of the diversity present in an environmental sample is out of reach at this time. Instead, metabarcoding is used today and it is expected that OTUs represent a category relevant for biodiversity inventories on a molecular basis. However, artefacts in the production of OTUs can occur at different stages and may impact ecological conclusions. We propose to evaluate the quality of OTUs in a sample by characterising the deviation of each OTU’s dissimilarity array from that of an ideal OTU where all sequences are at distances smaller than the barcoding gap. We consider two deviations: the creation of composed OTUs, corresponding to the artificial merging of several OTUs and the creation of noisy OTUs that contain some sequences that are loosely associated with the core sequence of the OTUs and that do not form a compact subgroup. We propose a simple and automatic 2-step method that successively categorises the OTUs of a sample as composed or single and then identifies OTUs with noise amongst the single ones. The associated code is available at https://forgemia.inra.fr/alain.franc/otu_shape. We applied the method on 32 samples of diatoms from Arcachon Bay (France) that represent contrasted environmental conditions and we obtained good agreement with expert categorisation of OTUs. We suggest that single OTUs without noise can be used as such for further ecological studies. Composed OTUs should be post-treated with classical clustering or community detection tools. The quality of single OTUs with noise remains to be further tested via supplementary studies on a diversity of organisms.

A recommendation of: Nicolas Bez, Pierre Gloaguen, Marie-Pierre Etienne, Rocio Joo, Sophie Lanco, Etienne Rivot, Emily Walker, Mathieu Woillez, Stéphanie Mahévas Proper account of auto-correlations improves decoding performances of state-space (semi) Markov models https://hal.science/hal-04547315v3

Probabilistic graphical models offer a powerful framework to account for the dependence structure between variables, which is represented as a graph. However, the dependence between variables may render inference tasks intractable. In this paper we review techniques exploiting the graph structure for exact inference, borrowed from optimisation and computer science. They are built on the principle of variable elimination whose complexity is dictated in an intricate way by the order in which variables are eliminated. The so-called treewidth of the graph characterises this algorithmic complexity: low-treewidth graphs can be processed efficiently. The first message that we illustrate is therefore the idea that for inference in graphical model, the number of variables is not the limiting factor, and it is worth checking for the treewidth before turning to approximate methods. We show how algorithms providing an upper bound of the treewidth can be exploited to derive a 'good' elimination order enabling to perform exact inference. The second message is that when the treewidth is too large, algorithms for approximate inference linked to the principle of variable elimination, such as loopy belief propagation and variational approaches, can lead to accurate results while being much less time consuming than Monte-Carlo approaches. We illustrate the techniques reviewed in this article on benchmarks of inference problems in genetic linkage analysis and computer vision, as well as on hidden variables restoration in coupled Hidden Markov Models.