Results 221 to 230 of about 99,052 (287)

Cameras do not always take a full picture: wolf activity patterns revealed by accelerometers versus road‐positioned camera traps

Remote Sensing in Ecology and Conservation, EarlyView.
Camera traps have become an increasingly popular non‐invasive alternative to animal‐attached devices for studying wildlife behaviour. This study compared wolf (Canis lupus) activity patterns derived from collar accelerometers and road‐positioned camera traps and revealed strong overall agreement but also important seasonal and diel mismatches between ...
Katarzyna Bojarska, Michał Żmihorski, Morteza Naderi, J. David Blount, Mark Chynoweth, Emrah Coban, Çağan H. Şekercioğlu, Josip Kusak   +7 more
wiley   +1 more source

From snapshots to continuous estimates: Augmenting citizen science with computer vision for fish monitoring

Remote Sensing in Ecology and Conservation, EarlyView.
This study presents an end‐to‐end computer‐vision pipeline for monitoring fish migration using underwater video. We integrate field camera deployment, annotation, model training and automated in‐season counting to generate continuous, high‐resolution data on river herring spawning migration.
Zhongqi Chen, Timm Haucke, Sara Beery, Keven Bennett, Austin Powell, Lydia Zuehsow, Robert Vincent, Linda Deegan   +7 more
wiley   +1 more source

Lacking data? No worries! How synthetic images can alleviate image scarcity in wildlife surveys: A case study with muskox (Ovibos moschatus)

Remote Sensing in Ecology and Conservation, EarlyView.
This study investigates the integration of synthetic imagery, created with diffusion‐based models, to supplement limited training data and improve muskox (Ovibos moschatus) detection in zero‐shot (ZS) and few‐shot (FS) settings. ZS models detected more than 80% of muskoxen in real images, confirming the potential of synthetic data as a substitute for ...
Simon Durand, Samuel Foucher, Alexandre Delplanque, Joëlle Taillon, Jérôme Théau   +4 more
wiley   +1 more source

Elephant seal dive behaviour responds consistently to changes in foraging success regardless of sex or ocean habitat. [PDF]

PeerJ
Green DB, Bestley S, McMahon CR, Lea MA, Harcourt RG, Guinet C, Hindell MA.   +6 more
europepmc   +1 more source

Semi‐automated seal detection on the Western Antarctic Peninsula: an unsupervised machine learning approach for detecting ice seals in aerial survey data

Remote Sensing in Ecology and Conservation, EarlyView.
This study presents a semi‐automated, rule‐based image analysis pipeline to detect ice seals in aerial surveys of the Western Antarctic Peninsula during an unusually low sea ice year. By using simple hierarchical clustering instead of deep learning, the method substantially reduced human annotation effort while achieving 82% recall, identifying 758 ...
Claire McGinnity, Connor C.G. Bamford, Nathan Fenney, Andrew Fleming, Jaume Forcada, Michael S. Tift, Luis A. Hückstädt, Daniel P. Costa, Peter T. Fretwell   +8 more
wiley   +1 more source

Temporal scale and conspecific density drive moonlight preferences of a nocturnal granivore. [PDF]

Behav Ecol
Zwolak R, Zduniak M, Celebias P, Wróbel A.   +3 more
europepmc   +1 more source

Human foraging strategies flexibly adapt to resource distribution and time constraints. [PDF]

Cogn Affect Behav Neurosci
Simonelli V, Nuzzi D, Lancia GL, Pezzulo G.   +3 more
europepmc   +1 more source

Optimized K-means algorithm for image segmentation based on improved dung beetle algorithm. [PDF]

Sci Rep
Li N, Luo Y, Feng Z, Qu H, Liu Z.
europepmc   +1 more source

Environmental dynamics impact whether matching is optimal. [PDF]

PNAS Nexus
Guo Y, Hermundstad AM.
europepmc   +1 more source

Frequency-Dependent Seed Selection: How Relative Abundance and Seed Traits Jointly Mediate Foraging Preference in Scatter-Hoarding Rodents. [PDF]

Ecol Evol
Guo K, Zhang J, Lu Z, Wang B.
europepmc   +1 more source