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dc.contributor.authorAlison, Jamie
dc.contributor.authorPayne, Stephanie
dc.contributor.authorAlexander, Jake M.
dc.contributor.authorBjorkman, Anne D.
dc.contributor.authorClark, Vincent Ralph
dc.contributor.authorGwate, Onalenna
dc.contributor.authorHuntsaar, Maria
dc.contributor.authorIseli, Evelin
dc.contributor.authorLenoir, Jonathan
dc.contributor.authorMann, Hjalte Mads Rosenstand
dc.contributor.authorSteenhuisen, Sandy-Lynn
dc.contributor.authorHøye, Toke Thomas
dc.date.accessioned2024-10-03T08:22:30Z
dc.date.available2024-10-03T08:22:30Z
dc.date.issued2023-12-09
dc.description.abstractMicroclimate—proximal climatic variation at scales of metres and minutes—can exacerbate or mitigate the impacts of climate change on biodiversity. However, most microclimate studies are temperature centric, and do not consider meteorological factors such as sunshine, hail and snow. Meanwhile, remote cameras have become a primary tool to monitor wild plants and animals, even at micro-scales, and deep learning tools rapidly convert images into ecological data. However, deep learning applications for wildlife imagery have focused exclusively on living subjects. Here, we identify an overlooked opportunity to extract latent, ecologically relevant meteorological information. We produce an annotated image dataset of micrometeorological conditions across 49 wildlife cameras in South Africa's Maloti-Drakensberg and the Swiss Alps. We train ensemble deep learning models to classify conditions as overcast, sunshine, hail or snow. We achieve 91.7% accuracy on test cameras not seen during training. Furthermore, we show how effective accuracy is raised to 96% by disregarding 14.1% of classifications where ensemble member models did not reach a consensus. For two-class weather classification (overcast vs. sunshine) in a novel location in Svalbard, Norway, we achieve 79.3% accuracy (93.9% consensus accuracy), outperforming a benchmark model from the computer vision literature (75.5% accuracy). Our model rapidly classifies sunshine, snow and hail in almost 2 million unlabelled images. Resulting micrometeorological data illustrated common seasonal patterns of summer hailstorms and autumn snow falls across mountains in the northern and southern hemispheres. However, daily patterns of sunshine and shade diverged between sites, impacting daily temperature cycles. Crucially, we leverage micrometeorological data to demonstrate that (1) experimental warming using open-top chambers shortens early snow events in autumn, and (2) imagederived sunshine marginally outperforms sensor-derived temperature when predicting bumblebee foraging. These methods generate novel micrometeorological variables in synchrony with biological recordings, enabling new insights from an increasingly global network of wildlife cameras.en_US
dc.identifier.citationAlison, Payne, Alexander, Bjorkman, Clark, Gwate, Huntsaar, Iseli, Lenoir, Mann, Steenhuisen, Høye. Deep learning to extract the meteorological by-catch of wildlife cameras. Global Change Biology. 2024;30(1)en_US
dc.identifier.cristinIDFRIDAID 2235399
dc.identifier.doi10.1111/gcb.17078
dc.identifier.issn1354-1013
dc.identifier.issn1365-2486
dc.identifier.urihttps://hdl.handle.net/10037/34992
dc.language.isoengen_US
dc.publisherWileyen_US
dc.relation.journalGlobal Change Biology
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2023 The Author(s)en_US
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0en_US
dc.rightsAttribution-NonCommercial 4.0 International (CC BY-NC 4.0)en_US
dc.titleDeep learning to extract the meteorological by-catch of wildlife camerasen_US
dc.type.versionpublishedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
Med mindre det står noe annet, er denne innførselens lisens beskrevet som Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)