The recent recognition of the Neotropical otter (Lontra annectens) as a distinct species highlights the need to evaluate its genetic status and connectivity across fragmented tropical habitats. We analyzed genetic diversity, population structure, and recent demographic patterns of L. annectens from two contrasting regions in northern Costa Rica-Tortuguero National Park (TNP) and the Sarapiqu & iacute; River Basin (SRB). Non-invasive fecal and anal-gland secretion samples collected during 2021-2022 were genotyped at ten nuclear DNA microsatellite loci. Genetic diversity was moderate across regions (mean allelic richness [AR] = 3.98-4.03, observed heterozygosity [Ho] = 0.52-0.58), expected heterozygosity [He] = 0.62-0.65) with no significant inter-regional differences. Bayesian clustering, principal component analysis, and pairwise FST (0.002) supported a near-panmictic population. Kinship analyses detected localized clusters of related individuals, suggesting weak but non-random structuring, while contemporary migration estimates indicated low-frequency, asymmetric gene flow from SRB to TNP. Bottleneck tests revealed signatures of recent demographic contraction in both regions, particularly in TNP. These findings demonstrate limited yet ongoing connectivity among riverine subpopulations and emphasize that increasing habitat fragmentation could erode this exchange. Maintaining hydrological corridors and monitoring genetically vulnerable subpopulations should be conservation priorities to preserve gene flow and long-term viability of L. annectens in northern Costa Rica.
This study proposes a novel, highly sensitive surface plasmon resonance (SPR) sensor based on a hexagonal photonic crystal fibre (PCF) sensor, tailored for analytes' refractive index (RI) detection from 1.30 to 1.43. The sensor's optical characteristics are analysed using finite element method (FEM) simulations. The proposed design achieves a peak wavelength sensitivity of 29,000 nm/RIU and an amplitude sensitivity of 2,653.68 RIU-& sup1;. Additionally, a high resolution of 3.45 & times; 10(-)(6) RIU underscores its effectiveness in capturing minute variations in refractive index. We conducted a comparative evaluation of ten machine learning algorithms for predicting confinement loss and amplitude sensitivity. Our results show that ensemble methods, particularly Extra Trees, Random Forest, Gradient Boosting, and XGBoost, achieve exceptionally high prediction accuracy for confinement loss (R-2 > 0.999), while LightGBM outperforms other models for amplitude sensitivity prediction (R-2 = 0.9448). The proposed sensor is stable and suited for detecting analytes for food safety, and adulteration monitoring applications.
Bark beetles contributing to ponderosa pine mortality in western North America are ecologically and economically impactful. Future climate patterns are expected to exacerbate these impacts. While reactive management aims to reduce the impact of bark beetle-caused tree mortality after these insects have already entered a forest system, preventative measures can serve to reduce the susceptibility of forested stands to epidemic bark beetle attacks before they occur. It is well documented that stressed trees are more susceptible to beetle attack and subsequent mortality than healthy trees. For this reason, nearly a century of research has been focused on the identification of quantifiable characteristics that serve as proxy variables indicative of tree stress. Dozens of bark beetle susceptibility models exist, though they vary widely in their use of variables, application, predictive capabilities, and region of interest. This review serves to identify bark beetle susceptibility models for ponderosa pine forests in western North America, highlight their limitations, and outline the improvements that can be made in the development of future susceptibility models.
Rapid urban growth in tropical megacities is putting serious pressure on critical ecosystem services, thereby complicating the implementation of sustainable urban planning frameworks. To better understand and address these challenges, we used artificial intelligence (AI) and satellite data to map and predict land-use changes in Chittagong City Corporation (CCC), Bangladesh, from 2000 to 2048. We combined Random Forest (RF) classification with a Cellular Automata–Artificial Neural Network (CA–ANN) model. The Random Forest method classified four land-cover types: vegetation, built-up areas, barren land, and open water bodies, with over 97
Numerous studies have compared public perceptions of climate and environmental change with instrument-derived meteorological records, yet methodological inconsistencies have limited clarity on the degree of alignment between the two. This systematic review synthesizes 204 peer-reviewed articles published between 2010 and 2025 to assess (i) how studies define and use perception recall periods, (ii) the temporal coverage and type of instrumental climate data used and (iii) the methods used to compare perceived and observed trends. Most studies focused on rainfall and temperature and were concentrated in Africa and Asia. We find that triangulation-based comparisons overwhelmingly dominate literature, with only a small subset employing statistical tests capable of assessing the strength or significance of alignment. Across studies, temporal mismatches between perception recall periods and climate data were common, and more than 100 studies did not report recall periods at all. Despite these limitations, many studies reported alignment between perceptions and instrumental records. Overall, our review recommends clearer reporting standards and broader adoption of statistical approaches that explicitly align respondents’ perception recall periods and duration lived in the study region with instrumental data to produce more robust and comparable assessments of perception–climate relationships.
