Presence-only versus presence-absence data in species composition determinant analyses
Presence-only versus presence-absence data in species composition determinant analyses
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DOI：
10.1111/j.1472-4642.2011.00755.x
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发表时间：
2011-05-01
影响因子：
4.6
通讯作者：
Carmel, Yohay
中科院分区：
文献类型：
--
作者：
Kent, Rafi;Carmel, Yohay
研究物种与其物理环境之间的关系需要物种分布数据，最好是基于调查得出的存在-不存在（P-A）数据。这些数据的空间范围有限。仅存在（P-O）数据被认为不适合此类分析。我们的目的是评估是否可以使用这样的数据时，考虑了众多的物种在一个大的空间范围内，为了分析环境因素和物种组成之间的关系。然而，所使用的数据的地理来源是连续的USA.MethodsWe创建的分布图50虚拟物种的实际环境条件的基础上，在研究中。取样地点是根据全球生物多样性信息机制的真实观察结果确定的。我们通过选择类似于1000个随机位置产生P-A数据，并记录所有物种的存在/不存在。我们制作了两个P-O数据集。通过在物种真实出现的位置对物种进行采样来产生完整的P-O集。部分P-O是与P-A数据集大小匹配的完整P-O数据集的子集。对于每个数据集，我们记录了相同位置的环境变量。我们使用CCA来评估由每个变量解释的物种组成的方差。我们评估的偏差，在数据集通过计算的偏差，平均值的环境变量在采样位置相比，整个area.ResultsP-A和P-O数据集的方差解释的不同的环境变量的量是相似的。我们发现相当大的环境和空间偏差的P-O数据集，相比，整个研究area.Main conclusionsOur结果表明，虽然P-O数据的集合包含偏见，众多的物种，从而相对大量的信息在数据中，允许使用的P-O数据分析环境的决定因素的物种组成。
AimStudying relationships between species and their physical environment requires species distribution data, ideally based on presence-absence (P-A) data derived from surveys. Such data are limited in their spatial extent. Presence-only (P-O) data are considered inappropriate for such analyses. Our aim was to evaluate whether such data may be used when considering a multitude of species over a large spatial extent, in order to analyse the relationships between environmental factors and species composition.LocationThe study was conducted in virtual space. However, geographic origin of the data used is the contiguous USA.MethodsWe created distribution maps for 50 virtual species based on actual environmental conditions in the study. Sampling locations were based on true observations from the Global Biodiversity Information Facility. We produced P-A data by selecting similar to 1000 random locations and recorded the presence/absence of all species. We produced two P-O data sets. Full P-O set was produced by sampling the species in locations of true occurrences of species. Partial P-O was a subset of full P-O data set matching the size of the P-A data set. For each data set, we recorded the environmental variables at the same locations. We used CCA to evaluate the amount of variance in species composition explained by each variable. We evaluated the bias in the data set by calculating the deviation of average values of the environmental variables in sampled locations compared to the entire area.ResultsP-A and P-O data sets were similar in terms of the amount of variance explained by the different environmental variables. We found sizable environmental and spatial bias in the P-O data set, compared to the entire study area.Main conclusionsOur results suggest that although P-O data from collections contain bias, the multitude of species, and thus the relatively large amount of information in the data, allow the use of P-O data for analysing environmental determinants of species composition.