Geology topics

Bilgecan Sen

Publications and source records attributed to Bilgecan Sen.

4 recordsLinked to original sources

USGS2026

An Antarctic ecosystem value index to quantify ecological value across trophic levels and over time

The Southern Ocean around Antarctica is one of the fastest changing regions on the planet and an emerging resource frontier for fisheries. Here, we present the Antarctic Ecosystem Value Index created by merging ecosystem information across food web trophic levels, from phytoplankton to fish and penguins, to quantify the ecological value of marine areas around the Antarctic continent. We find that coastal polynyas - areas of reduced sea-ice - have Index values 31–72% higher than surrounding areas, suggesting that these areas are biologically valuable hot spots for a number of ice-dependent Antarctic Species. Using output from an Earth system model to generate future projections of the Index, we find that high-value locations, often within polynyas, are likely to continue to be valuable throughout the 21st century despite environmental changes. The Antarctic Ecosystem Value Index indicates that penguins lose importance as their habitat becomes increasingly unsuitable, so protecting high-value habitat areas may be critical for these species. This study also shows that while many high-value Index areas are within existing or proposed Marine Protected Areas, there are several opportunities for adopting additional protection, particularly in East Antarctica and the Amundsen Sea.

Nature Communications↗

USGS2025

Living with uncertainty: Using multi-model large ensembles to assess emperor penguin extinction risk for the IUCN Red List

Improved methods for identifying species at risk are needed to strengthen climate change vulnerability assessments, as current estimates indicate that up to one million species face extinction due to environmental changes. Integrating multiple sources of uncertainty enhances the robustness of Red List of Threatened Species assessments, providing a more comprehensive understanding of species’ risks. We present a comprehensive framework that incorporates uncertainties, including measurement error, structural uncertainty, natural variability, future climate emissions scenario, and extreme events of sea ice loss, to evaluate the extinction risk of the emperor penguin ( Aptenodytes forsteri ), currently classified as Near-Threatened. We apply three ecological models, one bioclimatic and two metapopulation models, combined with a multi-model large ensemble (MMLE) of climate projections from general circulation models, to conduct a Red List evaluation at both global, regional and colony levels. Our results show that emperor penguins could be classified under a range of Red List categories depending on the ecological model, Intergovernmental Panel on Climate Change (IPCC) climate emissions scenario, and extreme event frequency. Under Criterion A, global classifications vary from Vulnerable to Critically Endangered. Severe declines are projected in the Indian and East Pacific sectors, Dronning Maud Land and the Amundsen-Bellingshausen Sea, with Criterion E indicating that 24% to 100% of colonies meet Endangered status thresholds, depending on huddling thresholds and ecological models. This study represents the first application of an MMLE coupled with an ecological ensemble approach to project climate change impacts on a species, capturing a comprehensive range of uncertainties and offering a framework for improving forecasting and decision-making under climate change.

Biological Conservation↗

USGS2024

The potential for species distribution models to distinguish source populations from sinks

1. While species distribution models (SDM) are frequently used to predict species occurrences to help inform conservation management, there is limited evidence evaluating whether habitat suitability can reliably predict intrinsic growth rates or distinguish source from sink populations. Filling this knowledge gap is critical for conservation science, as applications of SDMs for management purposes ultimately depend on these typically unobserved population or metapopulation dynamics. 2. Using regression, we associate previously published population level estimates of intrinsic growth and abundance derived from a Bayesian analysis of mark-recapture data for 17 bird species found in the contiguous United States with SDM habitat suitability estimates fitted here to opportunistic data for these same species. We then use AUC to measure how well SDMs can distinguish populations categorized as sources and sinks, depending on their intrinsic growth rates estimated from the mark-recapture data. We built SDMs using two different approaches, boosted regression trees (BRT) and Generalized Linear Models (GLM), and compared their predictive performance. Each SDM was built with presence points obtained from eBird and 10 environmental variables previously selected to model intrinsic growth rates and abundance for these species. 3. We show that SDMs built with opportunistic data are poor predictors of species demography in general; both BRT and GLM explained very little spatial variation of intrinsic growth rate and population abundance (median R2 across 17 species was close to 0.1 for both SDM methods). SDMs do, however, estimate higher suitability for source populations as compared to sinks. Out of 13 species which had both source and sink populations, both BRT and GLM had AUC values greater than 0.7 for 7 species when discriminating between sources and sinks. 4. Habitat suitability have the potential to be a useful measure to indicate a population’s ability to sustain itself as a source population, however more research on a diverse set of taxa is essential to fully explore this potential. This interpretation of habitat suitability can be particularly useful for conservation practice, and identification of explicit cases of when and how SDMs fail to match population demography can be informative for advancing ecological theory.

Journal of Animal Ecology↗

USGS2024

Detecting stochasticity in population time series using a non-parametric test of intrinsic predictability

1. Many ecological systems dominated by stochastic dynamics can produce complex time series that inherently limit forecast accuracy. The ‘intrinsic predictability’ of these systems can be approximated by a time series complexity metric called weighted permutation entropy (WPE). While WPE is a useful metric to gauge forecast performance prior to model building, it is sensitive to noise and may be biased depending on the length of the time series. Here, we introduce a simple randomized permutation test (rWPE) to assess whether a time series is intrinsically more predictable than white noise. 2. We apply rWPE to both simulated and empirical data to assess its performance and usefulness. To do this, we simulate population dynamics under various scenarios, including a linear trend, chaotic, periodic and equilibrium dynamics. We further test this approach with observed abundance time series for 932 species across four orders of animals from the Global Population Dynamics Database. Finally, using Adélie ( Pygoscelis adeliae ) and emperor penguin ( Aptenodytes forsteri ) time series as case studies, we demonstrate the application of rWPE to multiple populations for a single species. 3. We show that rWPE can determine whether a system is significantly more predictable than white noise, even with time series as short as 10 years that show an apparent trend under biologically realistic stochasticity levels. Additionally, rWPE has statistical power close to 100% when time series are at least 30 time steps long and show chaotic or periodic dynamics. Power decreases to ~10% under equilibrium dynamics, irrespective of time series length. Among four classes of animal taxa, mammals have the highest relative frequency (28%) of time series that are both longer than 30 time steps and indistinguishable from white noise in terms of complexity, followed by insects (16%), birds (16%) and bony fishes (11%). 4. rWPE is a straightforward and useful method widely applicable to any time series, including short ones. By informing forecasters of the inherent limitations to a system's predictability, it can guide a modeller's expectations for forecast performance.

Methods in Ecology and Evolution↗