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Utilización de Modelos Basados en Nichos para Mejorar el Muestreo de Especies Raras
Author(s) -
GUISAN ANTOINE,
BROENNIMANN OLIVIER,
ENGLER ROBIN,
VUST MATHIAS,
YOCCOZ NIGEL G.,
LEHMANN ANTHONY,
ZIMMERMANN NIKLAUS E.
Publication year - 2006
Publication title -
conservation biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.2
H-Index - 222
eISSN - 1523-1739
pISSN - 0888-8892
DOI - 10.1111/j.1523-1739.2006.00354.x
Subject(s) - sampling (signal processing) , rare species , niche , field (mathematics) , simple random sample , species distribution , environmental niche modelling , sample (material) , adaptive sampling , endangered species , computer science , ecological niche , ecology , habitat , statistics , mathematics , biology , population , monte carlo method , physics , demography , filter (signal processing) , sociology , pure mathematics , computer vision , thermodynamics
Because data on rare species usually are sparse, it is important to have efficient ways to sample additional data. Traditional sampling approaches are of limited value for rare species because a very large proportion of randomly chosen sampling sites are unlikely to shelter the species. For these species, spatial predictions from niche‐based distribution models can be used to stratify the sampling and increase sampling efficiency. New data sampled are then used to improve the initial model. Applying this approach repeatedly is an adaptive process that may allow increasing the number of new occurrences found. We illustrate the approach with a case study of a rare and endangered plant species in Switzerland and a simulation experiment. Our field survey confirmed that the method helps in the discovery of new populations of the target species in remote areas where the predicted habitat suitability is high. In our simulations the model‐based approach provided a significant improvement (by a factor of 1.8 to 4 times, depending on the measure) over simple random sampling. In terms of cost this approach may save up to 70% of the time spent in the field.