AIMC Topic: Introduced Species

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The impact of climate change on the invasiveness of Ageratum conyzoides (goat weed) in India: implications for biodiversity conservation.

Environmental monitoring and assessment
Climate change and biological invasions are major drivers of global biodiversity loss. Ageratum conyzoides L. is a highly aggressive invader, yet its ecological risks and potential range dynamics in India remain insufficiently quantified. To assess i...

Integrating climate scenarios and advanced modeling to predict freshwater fish invasions: insights from Carassius species in Iran.

Scientific reports
Freshwater ecosystems are increasingly imperiled by the dual pressures of biological invasions and climate change, necessitating robust predictive frameworks for effective management. This study integrates advanced ensemble machine learning (EML) wit...

Predicting the co-invasion of two Asteraceae plant genera in post-mining landscapes using satellite remote sensing and airborne LiDAR.

Scientific reports
The Asteraceae plant family includes the most widespread weedy invaders in Europe, which may jointly inhibit natural succession in degraded land under restoration. The complex local drivers of co-invasions hinder remote sensing (RS) monitoring effort...

An earth observation and explainable machine learning approach for determining the drivers of invasive species - a water hyacinth case study.

Environmental monitoring and assessment
Invasive species management is often constrained by limited resources and complicated by ecological and socio-economic variability across landscapes, leading to inconsistent outcomes. We use water hyacinth (Pontederia crassipes) in South Africa as a ...

Explainable few-shot learning workflow for detecting invasive and exotic tree species.

Scientific reports
Deep Learning methods are notorious for relying on extensive labeled datasets to train and assess their performance. This can cause difficulties in practical situations where models should be trained for new applications for which very little data is...

MAVSD: A Multi-Angle View Segmentation Dataset for Detection of Solidago Canadensis L.

Scientific data
Recent advancements in computer vision and deep learning have advanced automated vegetation monitoring, creating new opportunities for invasive species management. To this end, we introduce MAVSD (Multi-Angle View Segmentation Dataset), specifically ...

Machine learning-based habitat mapping of the invasive Prosopis juliflora in Sharjah, UAE.

Environmental monitoring and assessment
Prosopis juliflora, one of the most invasive trees, adversely affects the ecosystem and native plant communities in arid lands. This disrupts biodiversity and depletes water resources, posing significant ecological and economic challenges. Several at...

Forecasting invasive mosquito abundance in the Basque Country, Spain using machine learning techniques.

Parasites & vectors
BACKGROUND: Mosquito-borne diseases cause millions of deaths each year and are increasingly spreading from tropical and subtropical regions into temperate zones, posing significant public health risks. In the Basque Country region of Spain, changing ...

Characterizing feral swine movement across the contiguous United States using neural networks and genetic data.

Molecular ecology
Globalization has led to the frequent movement of species out of their native habitat. Some of these species become highly invasive and capable of profoundly altering invaded ecosystems. Feral swine (Sus scrofa × domesticus) are recognized as being a...

The implementation of robotic dogs in automatic detection and surveillance of red imported fire ant nests.

Pest management science
BACKGROUND: The Red Imported Fire Ant (RIFA), scientifically known as Solenopsis invicta, is a destructive invasive species causing considerable harm to ecosystems and generating substantial economic costs globally. Traditional methods for RIFA nests...