Using stacked SDMs with accuracy and rarity weighting to optimize surveys for rare plant species
- PDF / 757,668 Bytes
- 17 Pages / 439.37 x 666.142 pts Page_size
- 11 Downloads / 195 Views
(0123456789().,-volV)(0123456789().,-volV)
ORIGINAL PAPER
Using stacked SDMs with accuracy and rarity weighting to optimize surveys for rare plant species Hanna Rosner-Katz1
•
Jenny L. McCune1,2 • Joseph R. Bennett1
Received: 6 June 2019 / Revised: 11 July 2020 / Accepted: 19 July 2020 Ó Springer Nature B.V. 2020
Abstract Effective conservation of rare species requires reasonable knowledge of population locations. However, surveys for rare species can be time-intensive and therefore expensive. We test a methodology using stacked species distribution models (S-SDMs) to efficiently discover the greatest number of new rare species’ occurrences possible. We used S-SDMs for 22 rare plant species in southern Ontario, Canada to predict the best survey locations among individual 1-ha cells. For each cell, we weighted distribution model outputs by accuracy and species rarity to create an efficiency value. We used these efficiency values as an index to determine the locations of our field surveys. We conducted field surveys in multi-species cells, ‘‘MSC’’ (areas with high predicted efficiency for multiple species) and single species cells, ‘‘SSC’’ (areas with high probability for only one species) to determine the relative efficiency of a multi-species survey approach. MSC were more than twice as likely as SSC to have at least one rare plant species discovered. Efficiency ranks were also useful in directing surveyors toward incidental discoveries of other rare species that were not modeled. Our technique of using S-SDMs can help direct surveys to more efficiently find rare species occurrences. Keywords Prioritization Maxent Forest plants Model accuracy Conservation status
Communicated by Daniel Sanchez Mata. Electronic supplementary material The online version of this article (https://doi.org/10.1007/s10531-02002018-1) contains supplementary material, which is available to authorized users. & Hanna Rosner-Katz [email protected] 1
Department of Biology, Carleton University, 1125 Colonel By Drive, Ottawa, ON K1S 5B6, Canada
2
Present Address: Department of Biological Sciences, University of Lethbridge, 4401 University Drive, Lethbridge AB T1K 3M4, Canada
123
Biodiversity and Conservation
Introduction To effectively monitor and protect rare species, we must know the geographic locations of their populations, but many rare species lack precise distribution information. Field surveys are needed to fill these gaps (Peterson et al. 2011), but they are time-consuming and expensive (Lindenmayer et al. 2013). Thus, reliable protocols are needed to help efficiently direct survey efforts. Species distribution models have recently proliferated as conservation tools (Guisan et al. 2013). By using species occurrence data along with environmental or spatial predictors, the habitat suitability or probability of occurrence for a species can be predicted across an area of interest (Guisan and Zimmerman 2000; Elith et al. 2006). SDMs have also been used to predict current species richness (e.g. Guisan and
Data Loading...
