Biology subjects

Chamaille-Jammes, S.

Publications and source records attributed to Chamaille-Jammes, S..

23 records · Page 2Linked to original sources

bioRxiv2020

Revisiting giraffe photo-identification using deep learning and network analysis

An increasing number of ecological monitoring programs rely on photographic capture-recapture of individuals to study distribution, demography and abundance of species. Photo-identification of individuals can sometimes be done using idiosyncratic coat or skin patterns, instead of using tags or loggers. However, when performed manually, the task of going through photographs is tedious and rapidly becomes too time consuming as the number of pictures grows. Computer vision techniques are an appealing and unavoidable help to tackle this apparently simple task in the big-data era. In this context, we propose to revisit animal re-identification using image similarity networks and metric learning with convolutional neural networks (CNNs), taking the giraffe as a working example. We first developed an end-to-end pipeline to retrieve a comprehensive set of re-identified giraffes from about 4, 000 raw photographs. To do so, we combined CNN-based object detection, SIFT pattern matching, and image similarity networks. We then quantified the performance of deep metric learning to retrieve the identity of known individuals and detect unknown individuals never seen in the previous years of monitoring. After a data augmentation procedure, the re-identification performance of the CNN reached a Top-1 accuracy of about 90%, despite the very small number of images per individual in the training data set. While the complete pipeline succeeded in re-identifying known individuals, it slightly under-performed with unknown individuals. Fully based on open-source software packages, our work paves the way for further attempts to build automatic pipelines for re-identification of individual animals, not only in giraffes but also in other species.

ecology↗

bioRxiv2020

A theory of the use of information by enemies in the predator-prey space race

We currently lack a comprehensive theory about how behaviourally responsive predators and prey use the information they acquire about the environment and each others presence while engaged in the space race. This limits our understanding of the role of behaviour in trophic relationships and our ability to predict predators and prey distributions. Here we combined a simulation model with a genetic algorithm to discover how predators and prey behaving optimally should use information in environments with different levels of heterogeneity in prey forage distribution and prey vulnerability. Our results demonstrate the key role of movement unpredictability in successful strategies for both predators and prey, supporting the shell-game hypothesis. We however reveal striking differences between predators and prey in the magnitude of this unpredictability, and in how it varies with the environment. Our work offers a robust theoretical framework to better understand predator-prey space use and interpret empirical studies.

animal behavior and cognition↗

bioRxiv2019

A reformulation of the selection ratio shed light on resource selection functions and leads to a unified framework for habitat selection studies.

O_LIThe selection ratio (SR), i.e. the ratio of proportional use of a habitat over proportional availability of this habitat, has for long been the standard metric of habitat selection analyses. It is easy to compute and directly estimates disproportionate use. Its apparent restriction to habitat selection analyses using categorical predictors led to the development of the resource selection functions (RSF) approach, which has now become the norm. C_LIO_LIThe RSF approach has however led to debates and confusion. For instance, what functional form can be used remains debated, and the concept of relative probability of selection is often misunderstood. C_LIO_LII propose a reformulation of the SR demonstrating that it can be estimated in a regression context, and thus even with continuous predictors. This reformulation suggests that RSF can be seen as an intermediate step in the calculation of SR. This reformulation also clarifies some longstanding debates about RSF and data-selection/fitting practices. C_LIO_LII further suggest that SR estimates the strength of habitat selection, but that the contribution of selection in determining use, which should be more directly linked to fitness than selection per se, should be estimated by another metric, the selection effect on use (SE). SE could be estimated simply as the difference between proportional use and proportional availability, and can be computed from SR and a density estimation of availability. C_LIO_LII conduct a habitat selection analysis of plains zebras to demonstrate the added-value of going beyond RSF scores and using SR estimated in a regression context, and of combining SR and SE. C_LIO_LIOverall, I highlight the inter-relation between various metrics used to study habitat selection (i.e., SR, other selection indices, RSF scores, marginality). I conclude by proposing that SR and SE can be the unifying metrics of habitat selection, as together they offer a comprehensive view on the strength of habitat selection and its effect on habitat use. C_LI

ecology↗

bioRxiv2018

Identifying stationary phases in multivariate time-series for highlighting behavioural modes and home range settlements

O_LIRecent advances in bio-logging open promising perspectives in the study animal movements at numerous scales. It is now possible to record time-series of animal locations and ancillary data (e.g. activity level derived from on-board accelerometers) over extended areas and long durations with a high spatial and temporal resolution. Such time-series are often piecewise stationary, as the animal may alternate between different stationary phases (i.e. characterised by a specific mean and variance of some key parameter for limited periods). Identifying when these phases start and end is a critical first step to understand the dynamics of the underlying movement processes.\nC_LIO_LIWe introduce a new segmentation-clustering method we called segclust2d. It can segment bi-(or more generally multi-) variate time-series and possibly cluster the various segments obtained, corresponding to phases assumed to be stationary. It is easy to use, as it only requires specifying the minimum length of a segment (to prevent over-segmentation) based on biological considerations.\nC_LIO_LIAlthough this method can be applied to time-series of any nature, we focus here on two-dimensional piecewise time-series whose phases correspond at small scale to the expressions of different behavioural modes such as transit, feeding and resting, as characterised by two joint metrics such as speed and turning angles or, at larger scale, to temporary home ranges, characterised by stationary distributions of bivariate coordinates.\nC_LIO_LIUsing computer simulations, we show that segcust2d can rival and even outperform previous, more complex methods, which were specifically developed to highlight changes in movement modes or home range shifts (based on Hidden Markov or Ornstein-Uhlenbeck modelling, respectively), which, contrary to our method, require truly informative initial guesses to be efficient. Furthermore we demonstrate it on actual examples involving a zebras small scale movements and an elephants large scale movements, to illustrate the identification of various movement modes and of home range shifts, respectively.\nC_LI

ecology↗

bioRxiv2017

Zebra diel migrations reduce encounter risk with lions over selection for safe habitats

Diel migrations (DMs) undertaken by prey to avoid visual predators during the day have been demonstrated in many taxa in aquatic ecosystems. We reveal that zebras in Hwange National Park (Zimbabwe) employ a similar anti-predator strategy. Zebras forage near waterholes during the day but move away from them at sunset. We demonstrate that this DM, occurring over a few km, dramatically reduces their night-time risk of encountering lions, which generally remain close to waterholes. By contrast, zebra changes in night-time selection for vegetation types marginally reduced their risk of encountering lions. This may arise from a trade-off between encounter risk and vulnerability across vegetation types, with zebras favouring low vulnerability once DM has reduced encounter risk. In summary, here we (1) quantify the effect of a predator-induced DM in a terrestrial system on the likelihood of encountering a predator, (2) distinguish the effects of the DM from those related to day/night changes in selection for vegetation types. We discuss how revealing how prey partition their risk between predator encounter risk and habitat-driven vulnerability is likely critical to understand the emergence of anti-predator behavioural strategies.

ecology↗