Search bioRxiv⌕ Search

bioRxiv · 10.64898/2026.04.01.712451

Socotra Cormorants in the Arabian Gulf represent a large, but isolated population with low genetic diversity

Abstract

The Socotra Cormorant (Phalacrocorax nigrogularis) is a threatened seabird endemic to the coastal areas of the Arabian Gulf and the Arabian Sea, two regions separated by the Strait of Hormuz. Conserving threatened species requires clear delineation of population boundaries and the evaluation of genetic diversity. However, information on population structure and genetic variation, necessary for such an assessment, is lacking for the Socotra Cormorants. In this study, we assessed population structure and genetic diversity of Socotra Cormorants using two contrasting genetic markers: (1) maternally inherited mtDNA cytochrome oxidase 1 (COI) and (2) a nuclear non-coding region, {beta}-fibrinogen intron 7 (FIB7). A total of 279 individuals were sampled from four colonies in the Arabian Gulf and one colony on Hasikiyah Island in the Arabian Sea. Findings based on COI-variation suggest that the Arabian Gulf colonies represent one large population with extensive gene flow between Gulf colonies--except for the most distant pair of colonies--but isolated from Hasikiyah in the Arabian Sea. COI-variation indicated significant differentiation between the colonies inside the Gulf and the Hasikiyah colony. This is consistent with the reported distribution patterns, and may reflect phylogeographic processes of the region. The Gulf population showed substantially lower COI-diversity, with significantly lower nucleotide and haplotype diversity compared to Hasikiyah. In contrast, FIB7 results indicated extensive connectivity among colonies, with no detectable population structure or significant differences between the Gulf population and Hasikiyah. This study presents the first characterization of population structure and genetic diversity of Socotra Cormorants. The low genetic diversity coupled with relative isolation of the Gulf Socotra Cormorants raises conservation concerns regarding their long-term viability by potentially reducing fitness and eroding their evolutionary capacity to adapt to environmental change. LAY SUMMARYO_LIThe Socotra Cormorant is a threatened seabird found in the Arabian Gulf and Arabian Sea, but little was previously known about its population structure and genetic diversity. C_LIO_LIWe analyzed 279 birds from five nesting colonies (4 in the Gulf and 1 in the Arabian Sea), using two genetic markers to assess population connectivity and variation. C_LIO_LIWe found that the Socotra cormorants inside the Gulf appear to form a large, genetically isolated population with relatively low genetic diversity. C_LIO_LIThis is the first study that evaluates population structure and genetic diversity of this endangered seabird. C_LIO_LIThis is important information for the conservation of the Gulf Socotra cormorants because low genetic diversity, coupled with relative isolation, is associated with reduced fitness, and suggests that they may have a lower chance to adapt to environmental changes. C_LI

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Almansoori, N. M., Razali, H., Muzaffar, S. B., Chabanne, D. B. H., Natoli, A., Almusallami, M., Naser, H., Khamis, A., Al Harthi, F., Aldhaheri, L. S. R., Alaleeli, M. M. B., Al Diwani, F. M., Manlik, O.. 2026-04-03. Socotra Cormorants in the Arabian Gulf represent a large, but isolated population with low genetic diversity. https://doi.org/10.64898/2026.04.01.712451

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

Operationalising the context in regenerative agriculture: decision-making and farm variability

Soil degradation is a widespread challenge that requires a broad response at the individual farm level. To ensure effectivity, the practices should be tailored to the farm context: land manager objectives and farm specific challenges. These have however been difficult to quantify. Here we demonstrate that a workable farm context can be created based on a value survey, open satellite and soil data, and published models for vegetation gross primary productivity and soil erosion. Based on the findings, despite individual differences, farmers value profitability and operational efficiency, but also biodiversity and soil health. At least the regenerative farmers surveyed also value working for the greater good more than maintaining tradition or power. In spite of wide differences in farm production orientation, we also found that each farm also had a broad variation in individual fields GPP. Most fields have a stable GPP level, which is either high or low, and that there is a 2-3-fold difference between the weakest and best producing fields indicating the potential for improving GPP by improving the growing conditions on currently weak fields. In addition, soil loss was found to be highly concentrated in critical source areas, where 10% of the field area contributed to 50% of the soil loss. Overall, open data can be linked to modelling workflows to rapidly produce a decision-making context for farmers. This facilitates benchmarking and co-learning as well as enables land managers and advisors to identify the farm context for planning effective responses to soil degradation.

ecology↗

Fly, land, listen: Autonomous intermittent locomotion enables scalable low-noise drone ecoacoustic surveys

Ecoacoustic monitoring is enabling scientists and land managers to monitor and manage biodiversity more effectively and cost-efficiently in the face of human pressures and rapidly changing climates. Currently, most ecoacoustic surveys use manually deployed static sensors to record data, limiting the scale and reach of surveying efforts. Here we present a proof-of-concept autonomous drone platform that can use intermittent locomotion to conduct ecoacoustic surveys using an onboard sensor. Our custom prototype is able to fly, navigate, and avoid obstacles autonomously, land at a pre-determined location, record audio from an onboard microphone whilst static, before taking off and moving to the next sampling site. Autonomous navigation and operation enable greater sampling flexibility, reach, and scalability. Furthermore, by recording audio only whilst landed, noise from the drone's rotors does not mask signals or disturb animals, simplifying signal processing and downstream ecological analyses. We conducted trials in a scrubland habitat at the Knepp Estate in West Sussex, where our prototype demonstrated successful autonomous navigation and obstacle avoidance. Furthermore, we found that avian biodiversity data collected from the drone platform was comparable to that from traditional static acoustic sensor deployments, and that vocalisation patterns were not significantly impacted by the noise of the drone arriving or leaving a site. While scaled deployments of our technology would require further technical and regulatory challenges to be solved, our first demonstration of autonomous intermittent robotics-assisted ecoacoustic surveys lays the foundations for more cost-effective and far-reaching biodiversity surveys, with transformative potential for conservation, agricultural management, biosecurity, and more.

ecology↗

Do higher-order moments improve inference of population dynamics?

Fitting mathematical models of population dynamics to microbial time-series data allows us to estimate the ecological processes and interactions taking place in the microbiome. Repeated experiments of microbial systems yield replicates which slightly differ from each other. Some of this variability arises due to the fact that births and deaths occur at random. Most prior work focuses on fitting a deterministic mathematical model to the average across replicates. We use a stochastic model to fit the variability to the observed variability across replicates. Using a simulation-driven approach, we study the conditions under which our approach allows us to infer a larger fraction of ecological parameters correctly. We observe a substantial improvement in parameter inference. Lastly, our Bayesian approach not only allows us to incorporate prior information about the system, but also provides a distribution of parameters which conveys some idea of the uncertainty of the estimates.

ecology↗