Browse Articles

Contribution of environmental factors to genetic variation in the Pacific white-sided dolphin

Talwar et al. | Jan 23, 2024

Contribution of environmental factors to genetic variation in the Pacific white-sided dolphin
Image credit: Flavio

Here the authors sought to understand the effects of different variables that may be tied to pollution and climate change on genetic variation of Pacific white-sided dolphins, a species that is currently threatened by water pollution. Based on environmental data collected alongside a genetic distance matrix, they found that ocean currents had the most significant impact on the genetic diversity of Pacific white-sided dolphins along the Japanese coast.

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Idotea balthica comparison: Anatomy, locomotion, and seaweed preference of Massachusetts isopods

Yee et al. | Feb 17, 2022

<em>Idotea balthica</em> comparison: Anatomy, locomotion, and seaweed preference of Massachusetts isopods

Here the authors examined a population of Massachusetts marine isopods, seeking to classify them based on comparison of their morphology, movement, and seaweed preference compared to those of known species. In this process they found that they were most similar to Idotea balthica. The authors suggest that this knowledge combined with monitoring populations of marine biology such as these isopods in different physical and ecological areas can provide useful insight into the effects of climate change.

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Creating a drought prediction model using convolutional neural networks

Bora et al. | Oct 08, 2024

Creating a drought prediction model using convolutional neural networks
Image credit: The authors

Droughts kill over 45,000 people yearly and affect the livelihoods of 55 million others worldwide, with climate change likely to worsen these effects. However, unlike other natural disasters (hurricanes, etc.), there is no early detection system that can predict droughts far enough in advance to be useful. Bora, Caulkins, and Joycutty tackle this issue by creating a drought prediction model.

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Impact of Silverado Fire on soil carbon

Choi et al. | Feb 20, 2025

Impact of Silverado Fire on soil carbon

Soil stores three times more carbon than the atmosphere, making small changes in its storage and release crucial for carbon cycling and climate models. This study examined the impact of the 2020 California Silverado Fire on pyrogenic carbon (PyC) deposits using nitrogen and carbon isotopes as proxies. While the results showed significant variability in δ¹⁵N, δ¹³C, total carbon, and total nitrogen across sites, they did not support the hypothesis that wildfire increases δ¹⁵N while keeping δ¹³C constant, emphasizing the need for location-based controls when using δ¹⁵N to track PyC.

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The impact of timing and magnitude of the El Niño- Southern Oscillation on local precipitation levels and temperatures in the Bay Area

Li et al. | May 09, 2021

The impact of timing and magnitude of the El Niño- Southern Oscillation on local precipitation levels and temperatures in the Bay Area

Understanding the relationships between temperature, MEI, SPI, and CO2 concentration is important as they measure the major influencers of California’s regional climate: temperature, ENSO, precipitation, and atmospheric CO2. In this article, the authors analyzed temperature, Multivariate El Niño-Southern Oscillation Index (MEI), and Standard Precipitation Index (SPI) data from the San Francisco Bay Area from 1971 to 2016. They also analyzed CO2 records from Mauna Loa, HI for the same time period, along with the annual temperature anomalies for the Bay Area.

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Groundwater prediction using artificial intelligence: Case study for Texas aquifers

Sharma et al. | Apr 19, 2024

Groundwater prediction using artificial intelligence: Case study for Texas aquifers

Here, in an effort to develop a model to predict future groundwater levels, the authors tested a tree-based automated artificial intelligence (AI) model against other methods. Through their analysis they found that groundwater levels in Texas aquifers are down significantly, and found that tree-based AI models most accurately predicted future levels.

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