Forecasting sunflower grain yield a few weeks before crop harvesting is of strategic interest for cooperatives that collect and store grains. With such information, they can optimize their logistics and thus reduce the financial and environmental costs of grain storage. To provide these predictions, data assimilation approaches involving the crop model SUNFLO are used. The methods are based on the re-estimation of soil conditions and on the sequential update of crop model states using an ensemble Kalman filter. They combine the simulation of the crop model and time series of leaf area index (LAI) derived from remote sensors and extracted over 281 fields near Toulouse, France. A sensitivity analysis is used to identify the most relevant model inputs to consider into the data assimilation process. Results show that data assimilation leads to statistically significant better predictions than the simulation alone (from an RMSE of 9.88 q·ha${}^{-1}$ to an RMSE 7.49 q·ha${}^{-1}$). Significant improvement is achieved by relying on smoothed LAI rather than raw LAI. Nevertheless, there is still an over estimation of the grain yield that can be partially explained by the limiting factors observed on the fields and the forecast yield still need improvements to meet the required applications’ accuracy.

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.

Sheep farmed in grazing systems are usually exposed to seasonal feed quality fluctuations that are likely to increase with climate change. In this context,

This article introduces a new model, Gaussian Factorial Explicit Duration Hidden Markov Model (G-F-ED-HMM), that overcomes two limiting assumptions of hidden Markov models by allowing for flexible choice of distributions for the sojourn time, introducing semi-Markov dynamics, and by explicitly modelling a multidimensional hidden state with factorial structure. The combination of these two extensions is relevant for the representation of complex dynamics that can occur in ecology as well as in signal analysis. We provide a novel Variational EM (VEM) algorithm for parameter estimation of a G-F-ED-HMM, which is computationally efficient because we choose a variational conditional distribution in the family of independent hidden chains. The consequence is that the VM step relies only on quantities related to a monochain hidden semi-Markov model, which are straightforward to compute. We illustrate the behaviour of the VEM algorithm on simulated data sets inspired from the problem of inferring the routes of migratory birds. These experiments demonstrate that our VEM algorithm recovers parameters with low error and performs faster than a Monte Carlo EM algorithm.

Being able to predict the animal’s multiple traits responses to its environment is of high interest to study livestock adaptation to future conditions. In particular, trade-offs between traits are assumed to be a key determinant of animal robustness. Mechanistic models of energy allocation have been developed to predict potential trade-offs due to the competitive use of energy between biological functions, both within an individual’s life and among individuals. However, previous models have not explicitly integrated potential trade-offs between the mother and her offspring as well as among siblings. Yet those features are essential, particularly in suckling systems. Here we present an energy allocation model in sheep that incorporates the energy transfers between the mother and her offspring. The model simulates the trajectories of responses to feeding and herd management during the sheep’s lifetime. We first provide a detailed description of the model following a standard protocol. We then show that simulation results are in a realistic range in general. We finally illustrate how the model can represent individual variation in ewe-lamb trade-offs by varying the value of five input parameters controlling energy acquisition and allocation. Such parameterization allows to define different individual “types” of energy allocation strategies that are characterized by repeatable patterns of ewe and lamb output traits. This new sheep model provides the basis for future individual-based studies. Implications Addressing animal robustness in future environments is challenging due to the limited data availability on how multiple traits respond to constraining conditions. In this context, the sheep model presented here serves as a valuable tool for predicting the responses of various traits and their potential trade-offs throughout an animal’s lifetime. Its key innovation lies in incorporating trade-offs between the ewe and her lambs, as well as among lambs within a litter. This feature makes the model a strong basis for characterizing robust sheep in suckling systems.