Optimizing study design for multi‐species avian monitoring programmes. Issue 4 (8th May 2014)

• Record Type: Journal Article
• Authors: Sanderlin, Jamie S.; Block, William M.; Ganey, Joseph L.; Matthiopoulos, J.
• Is Part Of: Journal of applied ecology. Volume 51:Issue 4(2014)
• Page Start: 860
• Page End: 870
• Publication Date: 2014-05-08
• Subjects: Agriculture -- Periodicals; Biology, Economic -- Periodicals; Agricultural ecology -- Periodicals; Applied ecology -- Periodicals; 577
• DOI: 10.1111/1365-2664.12252
• Languages: English
• ISSNs: 0021-8901

Abstract

<abstract abstract-type="main" id="jpe12252-abs-0001"> <title>Summary</title> <p> <list id="jpe12252-list-0001" list-type="order"> <list-item> <p>Many monitoring programmes are successful at monitoring common species, whereas rare species, which are often of highest conservation concern, may be detected infrequently. Study designs that increase the probability of detecting rare species at least once over the study period, while collecting adequate data on common species, strengthen programme ability to address community‐wide hypotheses about how an ecosystem functions or responds to management actions. Study design guidelines exist for single‐species occupancy models, but practical guidance for monitoring species communities is needed. Single‐species population‐level designs are necessarily optimal for targeted species, whereas community study designs may be optimal for the assemblage of species, but not for every species within the community. Our objective was to provide a general optimization tool for multi‐species models and to illustrate this tool using data from two avian community studies.</p> </list-item> <list-item> <p>We conducted a simulation study with a Bayesian hierarchical model to explore design and cost trade‐offs for avian community monitoring programmes. We parameterized models using two long‐term avian studies from Arizona, USA and evaluated bias and accuracy for different combinations of species in the regional species pool and sampling design combinations of number of sites and sampling occasions. We optimized for maximum accuracy of species richness, detection probability and occupancy probability of rare species, given a fixed budget.</p> </list-item> <list-item> <p>Statistical properties for species richness and detection probability of species within the community were driven more by sampling occasions, whereas rare species occupancy probability was influenced more by percentage of area sampled. These results are consistent with results from single‐species models, suggesting some similarities between community‐level and single‐species models with occupancy and detection.</p> </list-item> <list-item> <p> <italic>Synthesis and applications</italic>. Study designs must be cost‐efficient while providing reliable knowledge. Researchers and managers have limited funds for collecting data. Our template can be used by researchers and managers to evaluate trade‐offs and efficiencies for allocating samples between the number of occasions and sites in multi‐species studies, and allow them to identify the optimal study design in order to achieve specific study objectives.</p> </list-item> </list> </p> </abstract>