A within-lake occupancy model for starry stonewort, Nitellopsis obtusa, to support early detection and monitoring

Alex Bajcz, Wesley Glisson, Jeffrey W. Doser, Daniel J Larkin, John R. Fieberg

Research output: Contribution to journalArticlepeer-review

Abstract

To efficiently detect aquatic invasive species early in an invasion when control may still be possible, predictions about which locations are likeliest to be occupied are needed at fine scales but are rarely available. Occupancy modeling could provide such predictions given data of sufficient quality and quantity. We assembled a data set for the macroalga starry stonewort (Nitellopsis obtusa) across Minnesota and Wisconsin, USA, where it is a new and high-priority invader. We used these data to construct a multi-season, single-species spatial occupancy model that included biotic, abiotic, and movement-related predictors. Distance to the nearest access was an important occurrence predictor, highlighting the likely role boats play in spreading starry stonewort. Fetch and water depth also predicted occupancy. We estimated an average detection probability of 63% at sites with mean non-N. obtusa plant cover, declining to ~ 38% at sites with abundant plant cover, especially that of other Characeae. We recommend that surveyors preferentially search for starry stonewort in areas of shallow depth and high fetch close to boat accesses. We also recommend searching during late summer/early fall when detection is likelier. This study illustrates the utility of fine-scale occupancy modeling for predicting the locations of nascent populations of difficult-to-detect species.

Original languageEnglish (US)
Article number2644
JournalScientific reports
Volume14
Issue number1
DOIs
StatePublished - Dec 2024

Bibliographical note

Publisher Copyright:
© 2024, The Author(s).

PubMed: MeSH publication types

  • Journal Article

Fingerprint

Dive into the research topics of 'A within-lake occupancy model for starry stonewort, Nitellopsis obtusa, to support early detection and monitoring'. Together they form a unique fingerprint.

Cite this