Publication Details |
| Category | Text Publication |
| Reference Category | Journals |
| DOI | 10.1111/ddi.13790 |
Licence  |
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| Title (Primary) | Dynamic species distribution models of Antarctic blue whales in the Weddell Sea using visual sighting and passive acoustic monitoring data |
| Author | El-Gabbas, A.
; Thomisch, K.; Van Opzeeland, I.; Burkhardt, E.; Boebel, O. |
| Source Titel | Diversity and Distributions |
| Year | 2024 |
| Department | BZF |
| Volume | 30 |
| Issue | 1 |
| Page From | 87 |
| Page To | 105 |
| Language | englisch |
| Topic | T5 Future Landscapes |
| Data and Software links | https://doi.org/10.1594/PANGAEA.959969 |
| Supplements | https://onlinelibrary.wiley.com/action/downloadSupplement?doi=10.1111%2Fddi.13790&file=ddi13790-sup-0001-AppendixS1.docx |
| Keywords | Antarctic blue whales; Balaenoptera musculus intermedia; dynamic species distribution models; maxent; PAM; passive acoustic monitoring; Southern Ocean; species distribution models; Weddell Sea |
| Abstract | Aim: Species distribution models (SDMs) are
essential tools in ecology and conservation. However, the scarcity of visual
sightings of marine mammals in remote polar areas hinders the effective
application of SDMs there. Passive acoustic monitoring (PAM) data provide
year-round information and overcome foul weather limitations faced by visual
surveys. However, the use of PAM data in SDMs has been sparse so far. Here, we
use PAM-based SDMs to investigate the spatiotemporal distribution of the
critically endangered Antarctic blue whale in the Weddell Sea. Location: The
Weddell Sea. Methods: We used presence-only dynamic SDMs employing visual
sightings and PAM detections in independent models. We compared the two
independent models with a third combined model that integrated both visual and
PAM data, aiming at leveraging the advantages of each data type: the extensive
spatial extent of visual data and the broader temporal/environmental range of
PAM data. Results: Visual and PAM data prove complementary, as indicated by a low spatial overlap between daily predictions and the low predictability of each model at detections of other data types. Combined data models reproduced suitable habitats as given by both independent models. Visual data models indicate areas close to the sea ice edge (SIE) and with low-to-moderate sea ice concentrations (SIC) as suitable, while PAM data models identified suitable habitats at a broader range of distances to SIE and relatively higher SIC.Main Conclusions: The results demonstrate the potential of PAM data to predict year-round marine mammal habitat suitability at large spatial scales. We provide reasons for discrepancies between SDMs based on either data type and give methodological recommendations on using PAM data in SDMs. Combining visual and PAM data in future SDMs is promising for studying vocalized animals, particularly when using recent advances in integrated distribution modelling methods. |
| Persistent UFZ Identifier | https://www.ufz.de/index.php?en=20939&ufzPublicationIdentifier=28474 |
| El-Gabbas, A., Thomisch, K., Van Opzeeland, I., Burkhardt, E., Boebel, O. (2024): Dynamic species distribution models of Antarctic blue whales in the Weddell Sea using visual sighting and passive acoustic monitoring data Divers. Distrib. 30 (1), 87 - 105 10.1111/ddi.13790 |
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