Machine learning and deep learning—A review for ecologists
The popularity of machine learning (ML), deep learning (DL) and artificial intelligence (AI)
has risen sharply in recent years. Despite this spike in popularity, the inner workings of ML …
has risen sharply in recent years. Despite this spike in popularity, the inner workings of ML …
Deep learning: Systematic review, models, challenges, and research directions
T Talaei Khoei, H Ould Slimane… - Neural Computing and …, 2023 - Springer
The current development in deep learning is witnessing an exponential transition into
automation applications. This automation transition can provide a promising framework for …
automation applications. This automation transition can provide a promising framework for …
Bioclip: A vision foundation model for the tree of life
Images of the natural world collected by a variety of cameras from drones to individual
phones are increasingly abundant sources of biological information. There is an explosion …
phones are increasingly abundant sources of biological information. There is an explosion …
Impacts of climate change on vegetation pattern: Mathematical modeling and data analysis
Climate change has become increasingly severe, threatening ecosystem stability and, in
particular, biodiversity. As a typical indicator of ecosystem evolution, vegetation growth is …
particular, biodiversity. As a typical indicator of ecosystem evolution, vegetation growth is …
Harnessing deep learning for population genetic inference
X Huang, A Rymbekova, O Dolgova, O Lao… - Nature Reviews …, 2024 - nature.com
In population genetics, the emergence of large-scale genomic data for various species and
populations has provided new opportunities to understand the evolutionary forces that drive …
populations has provided new opportunities to understand the evolutionary forces that drive …
A roadmap towards predicting species interaction networks (across space and time)
Networks of species interactions underpin numerous ecosystem processes, but
comprehensively sampling these interactions is difficult. Interactions intrinsically vary across …
comprehensively sampling these interactions is difficult. Interactions intrinsically vary across …
From identification to forecasting: the potential of image recognition and artificial intelligence for aphid pest monitoring
P Batz, T Will, S Thiel, TM Ziesche… - Frontiers in Plant …, 2023 - frontiersin.org
Insect monitoring has gained global public attention in recent years in the context of insect
decline and biodiversity loss. Monitoring methods that can collect samples over a long …
decline and biodiversity loss. Monitoring methods that can collect samples over a long …
The sensory ecology of speciation
DD Dell'Aglio, DF Rivas-Sánchez… - Cold Spring …, 2024 - cshperspectives.cshlp.org
In this work, we explore the potential influence of sensory ecology on speciation, including
but not limited to the concept of sensory drive, which concerns the coevolution of signals and …
but not limited to the concept of sensory drive, which concerns the coevolution of signals and …
Using deep learning to detect an indicator arid shrub in ultra-high-resolution UAV imagery
Effective monitoring of arid and semi-arid rangelands around the world is essential to
understand and combat degradation caused by anthropogenic use and facilitate effective …
understand and combat degradation caused by anthropogenic use and facilitate effective …
3D-POP-An automated annotation approach to facilitate markerless 2D-3D tracking of freely moving birds with marker-based motion capture
Recent advances in machine learning and computer vision are revolutionizing the field of
animal behavior by enabling researchers to track the poses and locations of freely moving …
animal behavior by enabling researchers to track the poses and locations of freely moving …