Species density models from opportunistic citizen science data
نویسندگان
چکیده
With the advent of technology for data gathering and storage, opportunistic citizen science are proliferating. Species distribution models (SDMs) aim to use species occurrence or abundance ecological insights, prediction management. We analysed a massive dataset with over 100,000 records incidental shipboard observations marine mammals. Our overall goal was create maps density from by using spatial regression count an effort offset. illustrate method two mammals in Gulf Alaska Bering Sea. counted total number animals 11,424 hexagons based on presence-only data. To decrease bias, we first estimated surface ship-days, which our proxy variable effort. used considerations pseudo-absences, left some as missing values. Next, created SDMs that modelled included offset second stage analysis example species, northern fur seals Steller sea lions. For both counts, random effects had multivariate normal conditional autoregressive (CAR) covariance matrix, providing 2.5 million Markov chain Monte Carlo (MCMC) samples (1,000 were retained) posterior distribution. novel MCMC scheme maintained sparse precision matrices observed when batch sampling also truncated stabilize estimates, look-up table autocorrelation parameter. These innovations allowed us draw several just few hours. From distributions SDMs, computed functions interest. normalized then applied estimate obtained literature derive spatially explicit especially within subsetted areas. ‘certain hotspots’ scaled local standard deviation thresholds. Hexagons values above threshold deemed hotspots enough evidence be certain about them.
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ژورنال
عنوان ژورنال: Methods in Ecology and Evolution
سال: 2021
ISSN: ['2041-210X']
DOI: https://doi.org/10.1111/2041-210x.13679