Occupancy model reveals limited detectability of lichens in a standardised large-scale monitoring
- Author:
- Hirschheydt G. von, Kéry M., Ekman S., Stofer S., Dietrich M., Keller C. & Scheidegger C.
- Year:
- 2024
- Journal:
- Journal of Vegetation Science
- Pages:
- 35: e13255 [11 p.]
- Url:
- https://doi.org/10.1111/jvs.13255
Question: What are the extent and the possible causes of imperfect detection in lichens? Because lichens are sessile and lack seasonality, they should be easier to survey than animals that can move or plants and fungi with seasonal morphology, and
one could therefore expect relatively high detection probabilities.
Location: 826 standardised sampling plots across Switzerland.
Methods: Using repeated detection/non-detection data from a national lichen survey
conducted by professional lichenologists, we estimated the mean and variation in detectability for 373 tree-living species with a multi-species occupancy model. We also
quantified the effect of species conspicuousness, identifiability and observer experience on detection probability.
Results: The average detection probability for a single survey was unexpectedly
low with an average of 0.49 (range across species: 0.25–0.74). Conspicuous species
showed higher average detectability (0.56) than inconspicuous species (0.41), and
identifiability as well as previous experience with a species substantially increased
the probability of a person detecting it. Accounting for experience, the mean detection probabilities of observers ranged from 0.32 to 0.69.
Conclusions: Our study confirms that detection probability per survey is often far
below 1 also in sessile organisms, even when a standardised survey is conducted by
experts. When species are seasonal (plants, fungi, etc.), survey areas are larger, or
field personnel are less experienced, as is the case for many surveys and monitoring
programs, detectabilities are likely to be substantially lower. We therefore argue that
imperfect detection should systematically be considered in the survey design and
data analysis also for sessile organisms.
Keywords: detection probability, epiphytic lichens, imperfect detection, lichen, monitoring, observer
error, occupancy model, repeated surveys, sampling error, sessile organism, Switzerland.
- Id:
- 36443
- Submitter:
- zpalice
- Post_time:
- Sunday, 28 April 2024 20:58