Sampling bees in tropical forests and agroecosystems: a review
Sampling bees in tropical forests and agroecosystems: a review
复制标题
热带森林和农业生态系统中的蜜蜂采样:综述
DOI:
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发表时间:
2017
影响因子:
1.9
通讯作者:
J. Collazo
中科院分区:
文献类型:
--
作者:
S. Prado;H. Ngo;Jaime A. Florez;J. Collazo
Bees are the predominant pollinating taxa, providing a critical ecosystem service upon which many angiosperms rely for successful reproduction. Available data suggests that bee populations worldwide are declining, but scarce data in tropical regions precludes assessing their status and distribution, impact on ecological services, and response to management actions. Herein, we reviewed >150 papers that used six common sampling methods (pan traps, baits, Malaise traps, sweep nets, timed observations and aspirators) to better understand their strengths and weaknesses, and help guide method selection to meet research objectives and development of multi-species monitoring approaches. Several studies evaluated the effectiveness of sweep nets, pan traps, and malaise traps, but only one evaluated timed observations, and none evaluated aspirators. Only five studies compared two or more of the remaining four sampling methods to each other. There was little consensus regarding which method would be most reliable for sampling multiple species. However, we recommend that if the objective of the study is to estimate abundance or species richness, malaise traps, pan traps and sweep nets are the most effective sampling protocols in open tropical systems; conversely, malaise traps, nets and baits may be the most effective in forests. Declining bee populations emphasize the critical need in method standardization and reporting precision. Moreover, we recommend reporting a catchability coefficient, a measure of the interaction between the resource (bee) abundance and catching effort. Melittologists could also consider existing methods, such as occupancy models, to quantify changes in distribution and abundance after modeling heterogeneity in trapping probability, and consider the possibility of developing monitoring frameworks that draw from multiple sources of data.