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Raising the bar for the next generation of biological atlases: using existing data to inform the design and implementation of atlas monitoring
Author(s) -
Mccabe Jennifer D.,
Anich Nicholas M.,
Brady Ryan S.,
Zuckerberg Benjamin
Publication year - 2018
Publication title -
ibis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.933
H-Index - 80
eISSN - 1474-919X
pISSN - 0019-1019
DOI - 10.1111/ibi.12561
Subject(s) - abundance (ecology) , sampling (signal processing) , distance sampling , sampling design , population , computer science , ecology , cartography , geography , biology , demography , filter (signal processing) , sociology , computer vision
Selecting a sampling design to monitor multiple species across a broad geographical region can be a daunting task and often involves tradeoffs between limited resources and the accurate estimation of population abundance and occurrence. Since the 1950s, biological atlases have been implemented in various regions to document the occurrence of plant and animal species. As next‐generation atlases repeat original surveys, investigators often seek to raise the rigour of atlases by incorporating species abundances. We present a repeatable framework that incorporates existing monitoring data, hierarchical modelling and sampling simulations to augment existing atlas occurrence and breeding status maps with a secondary sampling of species abundances. Using existing information on three bird species with varying abundance and detectability, we evaluated several sampling scenarios for the 2nd Wisconsin Breeding Bird Atlas. In general, we found that most sampling schemes produced accurate mean statewide abundance estimates for species with medium to high abundance and detection probability, but estimates varied significantly for species with low abundance and low detection probability. Our approach provided a statewide point‐count sampling design that: provided precise and unbiased abundance estimates for species of varied prevalence and detectability; ensured suitable spatial coverage across the state and its habitats; and reduced spending on total survey costs. Our framework could benefit investigators conducting atlases and other broad‐scale avian surveys that seek to add systematic, multi‐species sampling for estimating density and abundance across broad geographical regions.