Can large-scale patterns in insect atlas data predict local occupancy?

Coarse-grain atlas data are economical to collect, but fine-grain data contain more detail about the distribution of species within occupied cells. This information can change our perception of species’ distribution size, rarity and risk of extinction. Species occupancy describes the proportion of grid cells where a focal species is present, but occupancy depends on the spatial grain (grid cell area) of the units used to record species presences. Downscaling models have been developed to describe and extrapolate the relationship between spatial grain and occupancy (the occupancy-area curve, OAR), but have not previously been tested for highly mobile organisms. Here, we use atlas data for 38 British Odonata species. This taxon is highly mobile and also aggregated in the landscape due to a dependence on freshwater bodies for reproduction. Occupancy data at five coarse grains ≥ 100 km2 were used to parameterise 10 downscaling models. Predictive accuracy of the models were compared, using predicted and observed occupancy at the 1, 4 and 25 km2 grains. The Hui model gave the most accurate downscaling predictions across 114 species:grain combinations and gave the best predictions for 15 of the 38 species. Species-level traits were able to explain nearly 60% of the variation in in downscaling predictive error. Species with widespread, localised-aggregated and localised- sparse distributions were better predicted than species with a climatic range limit in Britain. The fine-grain occupancy of species with good dispersal abilities were poorly predicted by downscaling. Habitat generalists and specialists were better predicted than species with intermediate habitat breadth. Our results suggest that downscaling models, using widely available and economical coarse-grain atlas data, can provide sensitive, fine-grain estimates of distribution size, rarity, abundance and range change, even for high mobile taxa with a strong spatial structure.粗分辨率物种分布图集（coarse-grain atlas data）数据的采集成本经济实惠，但细分辨率数据能够提供更多关于已占据网格单元内物种分布的细节信息。这类信息可改变我们对物种分布范围、稀有性及灭绝风险的认知。物种占据率（occupancy）指的是目标物种存在的网格单元占比，而占据率会因记录物种存在的单元的空间分辨率（spatial grain，即网格单元面积）而异。现有研究已开发降尺度模型（downscaling models），用于描述并外推空间分辨率与占据率之间的关系——即占据面积曲线（occupancy-area curve, OAR），但此前尚未针对高度移动性生物开展过验证。 本研究使用了38种英国蜻蜓目（Odonata）物种的图集数据。该类群具有高度移动性，且因繁殖依赖淡水生境而在景观中呈聚集分布。我们采用5种分辨率≥100 km²的粗分辨率占据率数据，对10个降尺度模型进行参数化。随后以1 km²、4 km²和25 km²分辨率下的预测占据率与实测占据率进行对比，评估各模型的预测精度。 在114个物种-分辨率组合中，惠模型（Hui model）的降尺度预测精度最高，且在38个物种中有15个物种的预测效果最佳。物种水平的功能性状能够解释近60%的降尺度预测误差变异。相较于在英国存在气候分布范围限制的物种，分布呈广泛局域聚集及局域稀疏格局的物种，其预测效果更佳。具备良好扩散能力的物种，其细分辨率占据率的预测效果较差。生境泛化种与特化种的预测效果均优于生境宽度处于中间水平的物种。 本研究结果表明，借助广泛可得且成本低廉的粗分辨率图集数据构建的降尺度模型，即便针对具有强烈空间结构的高度移动性类群，也能够提供精准的细分辨率物种分布范围、稀有性、丰度及范围变化估计值。