Finding rare species and estimating the probability that all occupied sites have been found

Francois S Becker; Jasper A Slingsby; John Measey; Krystal A Tolley; Res Altwegg

doi:10.1002/eap.2502

Finding rare species and estimating the probability that all occupied sites have been found

Ecol Appl. 2022 Mar;32(2):e2502. doi: 10.1002/eap.2502. Epub 2022 Jan 28.

Authors

Francois S Becker^{1

2}, Jasper A Slingsby^{3

4}, John Measey⁵, Krystal A Tolley^{1

6}, Res Altwegg^{2

7}

Affiliations

¹ South African National Biodiversity Institute, Kirstenbosch Research Centre, Cape Town, South Africa.
² Centre for Statistics in Ecology, Environment and Conservation, Department of Statistical Sciences, University of Cape Town, Cape Town, South Africa.
³ Department of Biological Sciences and Centre for Statistics in Ecology, Environment and Conservation, University of Cape Town, Cape Town, South Africa.
⁴ Fynbos Node, South African Environmental Observation Network, Centre for Biodiversity Conservation, Cape Town, South Africa.
⁵ Centre for Invasion Biology, Department of Botany & Zoology, Stellenbosch University, Stellenbosch, South Africa.
⁶ Centre for Ecological Genomics and Wildlife Conservation, Department of Zoology, University of Johannesburg, Johannesburg, South Africa.
⁷ African Climate and Development Initiative, University of Cape Town, Cape Town, South Africa.

PMID: 34873777
DOI: 10.1002/eap.2502

Abstract

Detecting occupied sites of rare species, and estimating the probability that all occupied sites are known within a given area, are desired outcomes for many ecological or conservation projects. Examples include managing all occupied sites of a threatened species or eradicating an emerging invader. Occupied sites may remain undetected because (1) sites where the species potentially occurs had not been searched, and (2) the species could have been overlooked in the searched sites. For rare species, available data are typically scant, making it difficult to predict sites where the species probably occurs or to estimate detection probability in the searched sites. Using the critically endangered Rose's mountain toadlet (Capensibufo rosei), known from only two localities, we outline an iterative process aimed at estimating the probability that any unknown occupied sites remain and maximizing the chance of finding them. This includes fitting a species distribution model to guide sampling effort, testing model accuracy and sampling efficacy using the occurrence of more common proxy species, and estimating detection probability using sites of known presence. The final estimate of the probability that all occupied sites were found incorporates the uncertainties of uneven distribution, relative area searched, and detection probability. Our results show that very few occupied sites of C. rosei are likely to remain undetected. We also show that the probability of an undetected occupied site remaining will always be high for large unsearched areas of potential occurrence, but can be low for smaller areas intended for targeted management interventions. Our approach is especially useful for assessing uncertainty in species occurrences, planning the required search effort needed to reduce probability of unknown occurrence to desired levels, and identifying priority areas for further searches or management interventions.

Keywords: detection probability; model-based sampling; occupancy; rarity; species distribution model.

Publication types

Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

Animals
Endangered Species*
Probability