AIMC Topic: Biodiversity

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Inferring landscape-scale land-use impacts on rivers using data from mesocosm experiments and artificial neural networks.

PloS one
Identifying land-use drivers of changes in river condition is complicated by spatial scale, geomorphological context, land management, and correlations among responding variables such as nutrients and sediments. Furthermore, variations in standard me...

ENVIRONMENTS and EOL: identification of Environment Ontology terms in text and the annotation of the Encyclopedia of Life.

Bioinformatics (Oxford, England)
UNLABELLED: The association of organisms to their environments is a key issue in exploring biodiversity patterns. This knowledge has traditionally been scattered, but textual descriptions of taxa and their habitats are now being consolidated in centr...

Classifying fungi biodiversity using hybrid transformer models.

Journal of microbiological methods
Fungi are essential members of ecosystems, playing key roles in nutrient cycling, agriculture, and medicine. Their classification into proper species helps us to understand their biodiversity, allowing us to leverage their ecological and practical be...

Temporal insights into ecological community: Advancing waterbird monitoring with dome camera and deep learning.

Journal of environmental management
Biodiversity monitoring is critical for conservation and management. However, efficient species monitoring is often hindered by the complexities of ecological dynamics and the constraints of conventional techniques. This study presents an automated o...

Comparing point counts, passive acoustic monitoring, citizen science and machine learning for bird species monitoring in the Mount Kenya ecosystem.

Philosophical transactions of the Royal Society of London. Series B, Biological sciences
Biodiversity loss is a pressing challenge, with ecosystems across the world under threat from factors such as human encroachment, over exploitation and climate change. It is important to increase ecosystem monitoring efforts to provide actionable ins...

Opportunities and challenges for monitoring terrestrial biodiversity in the robotics age.

Nature ecology & evolution
With biodiversity loss escalating globally, a step change is needed in our capacity to accurately monitor species populations across ecosystems. Robotic and autonomous systems (RAS) offer technological solutions that may substantially advance terrest...

Efficient estimation of plant species diversity in desert regions using UAV-based quadrats and advanced machine learning techniques.

Journal of environmental management
Understanding the distribution of plant species diversity(PSD) along spatial and environmental gradients is essential for implementing effective conservation strategies. However, effective monitoring of large-scale PSD in desert regions remain challe...

Robotic monitoring of European habitats: a labeled dataset for plant detection in Annex I habitats of Italy.

Scientific data
The present data descriptor presents a dataset designed for the detection of plant species in various habitats of the European Union. This dataset is based on images captured using multiple different hardware including quadrupedal robot ANYmal C, ref...

Global intraspecific diversity of marine forests of brown macroalgae predicted by past climate conditions.

Communications biology
Global patterns of intraspecific genetic diversity are key to understanding evolutionary and ecological processes. However, insights into the distribution and drivers of genetic diversity remain limited, particularly for marine species. Here, we expl...

BioEncoder: A metric learning toolkit for comparative organismal biology.

Ecology letters
In the realm of biological image analysis, deep learning (DL) has become a core toolkit, for example for segmentation and classification. However, conventional DL methods are challenged by large biodiversity datasets characterized by unbalanced class...