Biology subjects

Spataro, B.

Publications and source records attributed to Spataro, B..

4 recordsLinked to original sources

bioRxiv2025

A MammaLian Demographic Database for comparative analyses of evolutionary biodemography: malddaba

The question of how individual age influences demographic transition rates, such as survival probabilities and reproduction rates, has long been a main question for demographers and evolutionary ecologists. This has resulted in the accumulation of studies estimating the age-specific demographic transition rates across the tree of life over the last few decades. Although this accumulation of studies has enabled comparative demographic analyses to be developed, such analyses remain difficult to perform because age-specific data are scattered across literature. Here, we present a new open-access database, malddaba, which compiles age-specific demographic rates for mammals in the wild from published information. Currently, the database encompasses 170 species from 250 publications, representing 428 age- and sex-specific survival series and 199 age- and sex-specific reproduction series. Each series is reported using a standardized approach aimed at facilitating the extraction and use of that dataset by anyone interested in comparative biology. We show how malddaba can be used to address a variety of questions, ranging from comparative ageing with the assessment of actuarial and reproductive senescence patterns in a wide diversity of mammals. We can also address questions related to comparative population dynamics. For this purpose, in addition to the raw demographic rate records, we were able to build 43 population-specific life tables using malddaba records, which allow demographic outputs to be estimated accurately, such as population growth rate and generation time for those species. The malddaba database will be regularly updated to keep adding new demographic estimates and bring a comprehensive and dynamic view of the diversity of demographic trajectories across mammals.

ecology↗

bioRxiv2023

The Silene latifolia genome and its giant Y chromosome

In some species, the Y is a tiny chromosome but the dioecious plant Silene latifolia has a giant [~]550 Mb Y chromosome, which has remained unsequenced so far. Here we used a hybrid approach to obtain a high-quality male S. latifolia genome. Using mutants for sexual phenotype, we identified candidate sex-determining genes on the Y. Comparative analysis of the sex chromosomes with outgroups showed the Y is surprisingly rearranged and degenerated for a [~]11 MY-old system. Recombination suppression between X and Y extended in a stepwise process, and triggered a massive accumulation of repeats on the Y, as well as in the non-recombining pericentromeric region of the X, leading to giant sex chromosomes. One-Sentence SummaryThis work uncovers the structure, function, and evolution of one of the largest giant Y chromosomes, that of the model plant Silene latifolia, which is almost 10 times larger than the human Y, despite similar genome sizes.

genomics↗

bioRxiv2022

The DeepFaune initiative: a collaborative effort towards the automatic identification of the French fauna in camera-trap images

Camera traps have revolutionized how ecologists monitor wildlife, but their full potential is realized only when the hundreds of thousands of collected images can be readily classified with minimal human intervention. Deep-learning classification models have allowed extraordinary progress towards this end, but trained models remain rare and are only now emerging for European fauna. We report on the first milestone of the DeepFaune initiative (https://www.deepfaune.cnrs.fr), a large-scale collaboration between more than 50 partners involved in wildlife research, conservation and management in France. We developed a classification model trained to recognize 26 species or higher-level taxa. The classification model achieved 0.97 validation accuracy and often >0.95 precision and recall for many classes. These performances were generally higher than 0.90 when tested on independent out-of-sample datasets for which we used image redundancy contained in sequence of images. We implemented our model in a software to classify images stored locally on a personal computer, so as to provide a free, user-friendly and high-performance tool for wildlife practitioners to automatically classify camera-trap images.

ecology↗

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↗