Understanding the causes of within- and among-population differences in vital rates, life histories, and population dynamics is a central topic in ecology. To understand how within- and among-population variation emerges, we need long-term studies that include episodic events and contrasting environmental conditions, data to characterize individual and shared variation, and statistical models that can tease apart shared and individual contribution to the observed variation. We used long-term tag-recapture data to investigate and estimate within- and among-population differences in vital rates, life histories, and population dynamics of marble trout Salmo marmoratus, an endemic freshwater salmonid with a narrow range. Only ten populations of pure marble trout persist in headwaters of Alpine rivers in western Slovenia. Marble trout populations are also threatened by floods and landslides, which have already caused the extinction of two populations in recent years. We estimated and determined causes of variation in growth, survival, and recruitment both within and among populations, and evaluated trade-offs between them. Specifically, we estimated the responses of these traits to variation in water temperature, density, sex, early life conditions, and extreme events. We found that the effects of population density on traits were mostly limited to the early stages of life and that growth trajectories were established early in life. We found no clear effects of water temperature on vital rates. Population density varied over time, with flash floods and debris flows causing massive mortalities (>55% decrease in survival with respect to years with no floods) and threatening population persistence. Apart from flood events, variation in population density within streams was largely determined by variation in recruitment, with survival of older fish being relatively constant over time within populations, but substantially different among populations. Marble trout show a fast to slow continuum of life histories, with slow growth associated with higher survival at the population level, possibly determined by food conditions and age at maturity. Our work provides unprecedented insight into the causes of variation in vital rates, life histories, and population dynamics in an endemic species that is teetering on the edge of extinction.

Download full-text PDF

Source
http://dx.doi.org/10.1890/15-1808.1DOI Listing

Publication Analysis

Top Keywords

vital rates
20
within- among-population
16
population dynamics
16
life histories
16
marble trout
16
rates life
12
histories population
12
population density
12
variation
9
population
9

Similar Publications

Background: Very-low-birth-weight infants (VLBWIs; birth weight < 1500 g) are at an increased risk of complicated influenza infection, which frequently includes pneumonia, encephalitis or even death. Data on influenza immunization and its outcome in VLBWIs are scarce. This study aimed to provide epidemiological data on influenza immunization for German VLBWIs and hypothesized that immunization would protect VLBWIs from infection-mediated neurodevelopmental impairment and preserves lung function at early school age.

View Article and Find Full Text PDF

Objectives: Accurate kidney and tumor segmentation of computed tomography (CT) scans is vital for diagnosis and treatment, but manual methods are time-consuming and inconsistent, highlighting the value of AI automation. This study develops a fully automated AI model using vision transformers (ViTs) and convolutional neural networks (CNNs) to detect and segment kidneys and kidney tumors in Contrast-Enhanced (CECT) scans, with a focus on improving sensitivity for small, indistinct tumors.

Methods: The segmentation framework employs a ViT-based model for the kidney organ, followed by a 3D UNet model with enhanced connections and attention mechanisms for tumor detection and segmentation.

View Article and Find Full Text PDF

The Neural Frontier of Future Medical Imaging: A Review of Deep Learning for Brain Tumor Detection.

J Imaging

December 2024

Laboratory of Automation and Manufacturing Engineering, Department of Industrial Engineering, Batna 2 University, Batna 05000, Algeria.

Brain tumor detection is crucial in medical research due to high mortality rates and treatment challenges. Early and accurate diagnosis is vital for improving patient outcomes, however, traditional methods, such as manual Magnetic Resonance Imaging (MRI) analysis, are often time-consuming and error-prone. The rise of deep learning has led to advanced models for automated brain tumor feature extraction, segmentation, and classification.

View Article and Find Full Text PDF

Background: Acute pancreatitis (AP) is a prevalent pathological condition of abdomen characterized by sudden onset, high incidence and complex progression. Timely assessment of AP severity is crucial for informing intervention decisions so as to delay deterioration and reduce mortality rates. Existing AP-related scoring systems can only assess current condition of patients and utilize only a single type of clinical data, which is of great limitation.

View Article and Find Full Text PDF

Accurate and timely diagnosis of t(9;22)-positive leukemias is vital to improving survival in pediatric patients. In low-resource settings, where healthcare disparities are exacerbated by limited resources, cost-effective and efficient diagnostic methods are essential for bridging these gaps and ensuring better outcomes. Among the diagnostic tools evaluated among 23 patients sample, RT-PCR demonstrated superior sensitivity (100%) and the shortest turnaround time (7 days), significantly outperforming FISH and karyotyping in both accuracy and timeliness.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!