Biology subjects

Gilardi, K.

Publications and source records attributed to Gilardi, K..

3 recordsLinked to original sources

bioRxiv2026

WildAlert: A Real-Time, AI-Driven Early Warning System for Wildlife Health and Ecological Threat Detection

Emerging infections and environmental disruptions increasingly threaten wildlife and ecosystem health. Free-ranging wildlife often serve as early indicators of ecological instability, making timely detection of morbidity and mortality events critical for early warning. Yet, existing systems lack the analytical capacity for real-time outbreak detection. We present WildAlert, an AI-driven early warning system that integrates fine-tuned BERT-based natural language processing models with unsupervised anomaly detection framework to identify unusual wildlife health events using real-time pre-diagnostic clinical data from wildlife rehabilitation organizations. The NLP module achieved high accuracy across clinical classifications and circumstances of admissions, enabling a pre-diagnostic syndromic surveillance framework. Retrospective validation demonstrated that WildAlert anomalies frequently coincided with or preceded confirmed morbidity events, including highly pathogenic avian influenza (HPAI), harmful algal bloom-associated toxicosis, cold-stunning in sea turtles, mass stranding events, West Nile virus, and mycoplasmosis. WildAlert establishes the worlds largest standardized, near real-time wildlife health surveillance system, transforming wildlife rehabilitation clinical records into actionable intelligence capable of detecting anomalies across taxa and regions, often before other surveillance methods. WildAlert provides a transferable analytical framework and scalable One Health model linking biodiversity monitoring, zoonotic disease preparedness, and ecosystem-linked environmental threats, with implications for conservation, public health and environmental hazard response.

ecology↗

bioRxiv2026

Non-invasive genomic sampling uncovers novel connectivities and origins of confiscated gorillas

BackgroundGorillas are a group of African great apes with two species and four subspecies that are currently critically endangered or endangered. Previous studies that analysed the genetics of wild gorillas from non-invasive samples, such as faeces or hair, analysed short mitochondrial or nuclear markers, which may not reflect the wider nuclear genome. Recent technical advances in target capture hybridisation, enrich the endogenous DNA content of non-invasive samples, allowing contiguous genomic regions to be sequenced. ResultsHere, we generated georeferenced genetic data from faecal and hair samples of 280 wild gorillas, sampled from three of the four gorilla subspecies, across large parts of their present-day distributions. With this expanded representation of gorilla genetic diversity in the wild, we detected three population clusters in western lowland gorillas, with the Sangha River and its affluents acting as significant barriers to gene flow. We reconstructed patterns of past population connectivity between western lowland gorillas in the north-eastern distribution range and Cross River gorillas, which may have been facilitated by a migration corridor also used by the Central and Nigeria-Cameroon chimpanzee subspecies. Finally, we predicted the geographic origins of wild-born gorillas, achieving a mean prediction error of 65 km, with a population-level resolution for mountain gorillas and some populations of western lowland gorillas. ConclusionOur work characterises fine-scale population structure in western lowland gorillas, which will be informative for future conservation strategies. This proof of concept in predicting geographic locations of wild gorillas, will be useful for future applications to geolocalise trafficked or rescued gorillas.

evolutionary biology↗

bioRxiv2020

The evolutionary history of ACE2 usage within the coronavirus subgenus Sarbecovirus

SARS-CoV-1 and SARS-CoV-2 are not phylogenetically closely related; however, both use the ACE2 receptor in humans for cell entry. This is not a universal sarbecovirus trait; for example, many known sarbecoviruses related to SARS-CoV-1 have two deletions in the receptor binding domain of the spike protein that render them incapable of using human ACE2. Here, we report three sequences of a novel sarbecovirus from Rwanda and Uganda which are phylogenetically intermediate to SARS-CoV-1 and SARS-CoV-2 and demonstrate via in vitro studies that they are also unable to utilize human ACE2. Furthermore, we show that the observed pattern of ACE2 usage among sarbecoviruses is best explained by recombination not of SARS-CoV-2, but of SARS-CoV-1 and its relatives. We show that the lineage that includes SARS-CoV-2 is most likely the ancestral ACE2-using lineage, and that recombination with at least one virus from this group conferred ACE2 usage to the lineage including SARS-CoV-1 at some time in the past. We argue that alternative scenarios such as convergent evolution are much less parsimonious; we show that biogeography and patterns of host tropism support the plausibility of a recombination scenario; and we propose a competitive release hypothesis to explain how this recombination event could have occurred and why it is evolutionarily advantageous. The findings provide important insights into the natural history of ACE2 usage for both SARS-CoV-1 and SARS-CoV-2, and a greater understanding of the evolutionary mechanisms that shape zoonotic potential of coronaviruses. This study also underscores the need for increased surveillance for sarbecoviruses in southwestern China, where most ACE2-using viruses have been found to date, as well as other regions such as Africa, where these viruses have only recently been discovered.

evolutionary biology↗