Biology subjects

Ostrowski, S.

Publications and source records attributed to Ostrowski, S..

4 recordsLinked to original sources

bioRxiv2025

Pollination and abiotic stress alter the distribution of variation in floral longevity and opportunities for adaptation

Background and aimsFloral longevity is thought to evolve by natural selection imposed by pollinators on heritable variation and resource constraints. In species with pollination-induced wilting, pollination rates should also influence phenotypic expression of genetic variation in longevity, raising questions of how and when variation in floral lifespan is shaped by adaptation. MethodsWe experimentally manipulated pollination rates in a wild population of Sabatia angularis (Gentianaceae). We asked how treatments influenced pollen gain curves, the phenotypic distribution of floral longevity, trait correlations, and potential costs. We further asked how abiotic factors influence longevity and how they may interact with pollination conditions. Key resultsMean, variance, and maximum of longevity expressed increased with declining pollination. A flower longevity-number trade-off and positive longevity-flower size relationship persisted regardless of pollination context. Plants with more--not longer-lived--flowers suffered greater seed predation, potentially mitigating the flower number-longevity trade-off. The vapor pressure deficit (VPD) and seasonal changes constrained longevity. VPD had the strongest effect under the poorest pollination conditions, where longer-lived flowers would otherwise be favored. ConclusionsOur findings highlight how ecological conditions and internal constraints shape phenotypic variation. Conditional expression of longevity may preserve genetic variation, enabling rapid evolutionary responses when environmental change causes selection and exposure of variation to align.

evolutionary biology↗

bioRxiv2025

Advancing Snow Leopard Density Estimates to Assess Population Dynamics Over Time in Afghanistan

Effective wildlife monitoring requires innovative methodologies to ensure accurate population assessments for conservation planning. This study focuses on snow leopards (Panthera uncia), a species classified as Vulnerable by the IUCN due to habitat loss, climate change, and over-exploitation. Employing spatial capture-recapture (SCR) models and AI-assisted individual identification, we analyzed camera trapping data from 2012-2013 and 2017-2019 to assess changes in snow leopard density and abundance in Wakhan National Park, Afghanistan. Our analysis used single-session and multi-session models to provide a comprehensive view of population dynamics. Single-session models revealed a substantial increase in density, with the full model estimate rising from 1.38 {+/-} 0.39 SE to 3.57 {+/-} 0.88 SE, while right-only and left-only models showed increases from 1.43 {+/-} 0.53 SE to 3.38 {+/-} 1.01 SE and from 1.04 {+/-} 0.36 SE to 2.21 {+/-} 0.72 SE, respectively. Our findings also highlight the importance of incorporating bilateral asymmetry into population estimates to avoid overestimation. For single-session models, the full model density estimate was 11.7% higher in 2012-2013 and 27.8% higher in 2017-2019 compared to the average of the left-only and right-only estimates. For multi-session models, the full model density estimate was 13.0% higher in 2012-2013 and 46.9% higher in 2017-2019. This emphasizes the importance of addressing asymmetry to avoid potential overestimation. This study demonstrates the potential of combining advanced analytical frameworks with AI to improve wildlife population assessments. By refining SCR methodologies and addressing key biases, these methods provide critical insights for snow leopard conservation and broader applications to wildlife monitoring. While the results indicate a significant increase in snow leopard populations, continued monitoring and long-term studies are essential to account for changes due to environmental and anthropogenic factors.

ecology↗

bioRxiv2023

Extreme in Every Way: Exceedingly Low Genetic Diversity in Snow Leopards Due to Persistently Small Population Size

Snow leopards (Panthera uncia) serve as an umbrella species whose conservation benefits their high-elevation Asian habitat. Their numbers are believed to be in decline due to numerous Anthropogenic threats; however, their conservation is hindered by numerous knowledge gaps. They are the least studied genetically of all big cat species with more to learn regarding their population structure, historical population size, and current levels of genetic diversity. Here, we use whole-genome sequencing data for 41 snow leopards (37 newly sequenced) to offer new insights into these unresolved questions. Among our samples, we find evidence of a primary genetic divide between the northern and southern part of the range around the Dzungarian Basin, as previously identified, and a secondary divide south of Kyrgyzstan around the Taklamakan Desert. Most noteworthy, we find that snow leopards have the lowest genetic diversity of any big cat species, due to a persistently small population size (relative to other big cat species) throughout their evolutionary history rather than recent inbreeding. Without a large population size or ample standing genetic variation to help buffer them from any forthcoming Anthropogenic challenges, snow leopard persistence may be more tenuous than currently appreciated.

evolutionary biology↗

bioRxiv2022

Comparison of Two Individual Identification Algorithms for Snow Leopards (Panthera uncia) after Automated Detection

1. Photo-identification of individual snow leopards (Panthera uncia) is the primary technique for density estimation for the species. A high volume of images from multiple projects, combined with pre-existing historical catalogs, has made identifying snow leopard individuals within the images cost- and time-intensive. 2. To speed the classification among a high volume of photographs, we trained and evaluated image classification methods for PIE v2 (a triplet loss network), and we compared PIEs accuracy to the HotSpotter algorithm (a SIFT based algorithm). Analyzed data were collected from a curated catalog of free-ranging snow leopards photographed across years (2012-2019) in Afghanistan and from samples in captivity provided by zoos from Finland, Sweden, Germany, and the United States. 3. Results show that PIE outperforms HotSpotter. We also found weaknesses in the initial PIE model, like a minor amount of background matching, which was addressed, although likely not fully resolved, by applying background subtraction (BGS) and left-right mirroring (LR) methods. The PIE BGS LR model combined with Hotspotter showed a Rank-1: 85%, Rank-5: 95%, Rank-20: 99%. 4. Overall, our results recommend implementing PIE v2 simultaneously with HotSpotter on Whiskerbook.org.

bioinformatics↗