The effect of network size and sampling completeness in depauperate networks
The effect of network size and sampling completeness in depauperate networks
复制标题
贫困网络中网络规模和采样完整性的影响
DOI：
10.1111/1365-2656.12912
复制
发表时间：
2018
影响因子：
4.8
通讯作者：
M. McGeoch
中科院分区：
文献类型：
--
作者：
Marie V. Henriksen;D. G. Chapple;S. Chown;M. McGeoch
The accurate estimation of interaction network structure is essential for understanding network stability and function. A growing number of studies evaluate under-sampling as the degree of sampling completeness (proportional richness observed). How the relationship between network structural metrics and sampling completeness varies across networks of different sizes remains unclear, but this relationship has implications for the within- and between-system comparability of network structure. Here, we test the combined effects of network size and sampling completeness on the structure of spatially distinct networks (i.e., subwebs) in a host-parasitoid model system to better understand the within-system variability in metric bias. Richness estimates were used to quantify a gradient of sampling completeness of species and interactions across randomly subsampled subwebs. The combined impacts of network size and sampling completeness on the estimated values of twelve unweighted and weighted network metrics were tested. The robustness of network metrics to under-sampling was strongly related to network size, and sampling completeness of interactions were generally a better predictor of metric bias than sampling completeness of species. Weighted metrics often performed better than unweighted metrics at low sampling completeness; however, this was mainly evident at large rather than small subweb size. These outcomes highlight the significance of under-sampling for the comparability of both unweighted and weighted network metrics when networks are small and vary in size. This has implications for within-system comparability of species-poor networks and, more generally, reveals problems with under-sampling ecological networks that may otherwise be difficult to detect in species-rich networks. To mitigate the impacts of under-sampling, more careful considerations of system-specific variation in metric bias are needed.
DOI：
--
发表时间：
2015-10
期刊：
--
影响因子：
--
作者：
Douglas M. Bates;M. Maechler;Ben Bolker;Steven C. Walker
通讯作者：
Douglas M. Bates;M. Maechler;Ben Bolker;Steven C. Walker
影响因子：
3.4
作者：
Fruend, Jochen;McCann, Kevin S.;Williams, Neal M.
通讯作者：
Williams, Neal M.