Improving species distribution models of zoonotic marine parasites
- Environmental niche modelling is an acclaimed method for estimating species’ present or future distributions. However, in marine environments the assembly of representative data from reliable and unbiased occurrences is challenging. Here, we aimed to model the environmental niche and distribution of marine, parasitic nematodes from the Pseudoterranova decipiens complex using the software Maxent. The distribution of these potentially zoonotic species is of interest, because they infect the muscle tissue of host species targeted by fisheries. To achieve the best possible model, we used two different approaches. The land distance (LD) model was based on abiotic data, whereas the definitive host distance (DHD) model included species-specific biotic data. To assess whether DHD is a suitable descriptor for Pseudoterranova spp., the niches of the parasites and their respective definitive hosts were analysed using ecospat. The performance of LD and DHD was compared based on the variables’ contribution to the model. The DHD-model clearly outperformed the LD-model. While the LD-model gave an estimate of the parasites’ niches, it only showed the potential distribution. The DHD-model produced an estimate of the species’ realised distribution and indicated that biotic variables can help to improve the modelling of data-poor, marine species.
Download full text files
Additional Services
| Author: | Katharina AltORCiDGND, Judith KochmannORCiD, Sven KlimpelORCiDGND, Sarah CunzeORCiDGND |
|---|---|
| URN: | urn:nbn:de:hebis:30:3-512588 |
| DOI: | https://doi.org/10.1038/s41598-019-46127-6 |
| ISSN: | 2045-2322 |
| Pubmed Id: | https://pubmed.ncbi.nlm.nih.gov/31285445 |
| Parent Title (English): | Scientific reports |
| Publisher: | Macmillan Publishers Limited, part of Springer Nature |
| Place of publication: | [London] |
| Document Type: | Article |
| Language: | English |
| Year of Completion: | 2019 |
| Date of first Publication: | 2019/07/08 |
| Publishing Institution: | Universitätsbibliothek Johann Christian Senckenberg |
| Release Date: | 2019/09/30 |
| Tag: | Biogeography; Ecological modelling |
| Volume: | 9 |
| Issue: | 1, Art. 9851 |
| Page Number: | 10 |
| First Page: | 1 |
| Last Page: | 10 |
| Note: | Open Access: This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
| HeBIS-PPN: | 454161212 |
| Institutes: | Angeschlossene und kooperierende Institutionen / Senckenbergische Naturforschende Gesellschaft |
| Biowissenschaften / Institut für Ökologie, Evolution und Diversität | |
| Fachübergreifende Einrichtungen / Biodiversität und Klima Forschungszentrum (BiK-F) | |
| Dewey Decimal Classification: | 5 Naturwissenschaften und Mathematik / 59 Tiere (Zoologie) / 590 Tiere (Zoologie) |
| Sammlungen: | Universitätspublikationen |
| Open-Access-Publikationsfonds: | Biowissenschaften |
| Licence (German): |  Creative Commons - Namensnennung 4.0 |
