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• W2621271340 abstract "Measuring population changes and trends is essential to identify threatened species, and is requested by several environmental regulations (e.g. European Habitat Directive). However, obtaining this information for small and cryptic animals is challenging, and requires complex, broad-scale monitoring schemes. How should we allocate the limited resources available for monitoring, to maximize the probability of detecting declines? The analysis of simulated data can help to identify the performance of monitoring scenarios across species with different features. We simulated data of populations with a wide range of abundance, detection probability and rate of decline, and tested under which circumstances open-population N-mixture models can successfully detect the decline of populations. We tested multiple monitoring strategies, to identify the ones having the highest probability of detecting declines. If 30 sites are surveyed, strong declines (≥30%) can be successfully spotted for nearly all the simulated species, except the species with lowest abundance and detection probability. Weaker declines are successfully identified only in species that are easy to detect and have high abundance. Increasing the number of sites quickly increases model power, but hundreds of sites would require monitoring to measure trends of the least detectable species. For most of species, performance of monitoring was improved by: surveying many sites with a few replicates per site; surveying many small sites instead of a few large sites; combining data from sites monitored for multiple species. Our findings show that one single monitoring approach cannot be appropriate for all the species, and that surveying efforts should be modulated across them, according to their detection probabilities and abundances. We provide quantitative values on how the number of surveys and the number of sites to be surveyed can be assigned to different species, and emphasize the need of planning to maximize the performance of monitoring." @default.
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• W2621271340 date "2017-06-01" @default.
• W2621271340 modified "2023-09-25" @default.
• W2621271340 title "Optimizing monitoring schemes to detect trends in abundance over broad scales" @default.
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• W2621271340 doi "https://doi.org/10.1111/acv.12356" @default.
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