Estimating species distributions from spatially biased citizen science data
Estimating species distributions from spatially biased citizen science data
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DOI：
10.1016/j.ecolmodel.2019.108927
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发表时间：
2020-04-15
影响因子：
3.1
通讯作者：
Baillie, Stephen R.
中科院分区：
文献类型：
--
作者：
Johnston, Alison;Moran, Nick;Baillie, Stephen R.
生态公民科学数据在生态学和保护领域的可获得性和应用正在迅速增长。许多公民科学项目的参与者可以灵活地选择他们调查的地点，这导致了更多的参与者，但也产生了空间上的偏差数据。评估这些有空间偏差的数据在多大程度上可以提供物种分布的可靠估计是很重要的。在这里，我们量化了一个公民科学项目中选址偏差的程度，以及这种空间偏差对物种分布模型的影响。利用2007年至2011年英国BirdTrack公民科学项目的数据，我们对数据提交的空间偏差进行了建模。接下来，我们建立了138种鸟类的物种占用模型，并评估了考虑空间偏差的影响。我们将这些分布与使用来自同一地区和时期的Atlas调查的无偏数据产生的分布进行了比较。对138个物种进行平均计算，带有空间偏差数据的模型对英国大多数地区的物种占用率做出了准确而精确的估计。然而，在苏格兰高地，这些分布既不太准确，也不太精确，平均显示出正偏差。考虑到空间偏差抽样的权重导致苏格兰高地的平均精度更高，但没有提高精度。该地区的环境特征明显，观测密度低，因此很难描述环境与物种占用的关系。考虑到空间偏差抽样并不影响全国大部分地区的平均准确度或精度。空间偏倚的公民科学数据可用于估算环境关系稳定且采样良好的区域的物种占用率。从空间偏倚数据估计的物种分布的可靠性需要在一系列不同的情景下进一步验证和测试。
Ecological citizen science data are rapidly growing in availability and use in ecology and conservation. Many citizen science projects have the flexibility for participants to select where they survey, resulting in more participants, but also spatially biased data. It is important to assess the extent to which these spatially biased data can provide reliable estimates of species distributions. Here we quantify the extent of site selection bias in a citizen science project and the implications of this spatial bias in species distribution models. Using data from the BirdTrack citizen science project in Great Britain from 2007 to 2011, we modelled the spatial bias of data submissions. We next produced species occupancy models for 138 bird species, and assessed the impact of accounting for spatial bias. We compared the distributions to those produced using unbiased data from an Atlas survey from the same region and time period. Averaging across 138 species, models with spatially biased data produced accurate and precise estimates of species occupancy for most locations in Great Britain. However, these distributions were both less accurate and less precise in the Scottish Highlands, showing on average a positive bias. Accounting for the spatially biased sampling with weights led to on average greater accuracy in the Scottish Highlands, but did not increase precision. This region is both distinct in environmental characteristics and has a low density of observations, making it difficult to characterise environmental relationships with species occupancy. Accounting for the spatially biased sampling did not affect average accuracy or precision throughout most of the country. Spatially biased citizen science data can be used to estimate species occupancy in regions with stationary environmental relationships and good sampling across environmental space. The reliability of estimated species distributions from spatially biased data should be further validated and tested under a range of different scenarios.