The present study provides energy density (ED) data and models for four macrobenthic invertebrate genera inhabiting freshwater lotic environments (Baetis, Hydropsyche, Rhyacophila, and Onychogomphus). Samples were collected in the hydrological freshwater network of the Region Friuli Venezia Giulia within different watercourse types (creeks, streams, rivers channels, and ditches), and energy density was directly measured using an adiabatic bomb calorimeter. Measured ED expressed in Joule g⁻¹ wet weight was strongly and positively correlated with percentage of dry weight (DW%) for all genera investigated (r² > 0.9504), allowing to obtain genus-specific predictive models based on the relationship between ED and DW%. Models were validated and showed good predictive power, as 90th percentile of observed percentage errors ranged between 4.23% and 5.18% while medians ranged between 1.32% and 2.83%. ANCOVA disclosed significant differences between the models, as those for Rhyacophila and Onychogomphus differed significantly from the others. The empirical models were used to build a dataset of estimated energy density, to assess the relationship between energy density and ecological status of the monitored riverine systems, assessed in compliance with European and Italian law by the application of four different ecological indices (ICMi, RQE_IBMR, STAR_ICMi, and LIMeco). Information regarding ED levels for freshwater macrobenthic invertebrates is still neglected in biomonitoring programs, but it could be useful to interpret some ecological situations in the context of ecological status assessment, especially in relation to the trophic condition of the investigated riverine systems.

本研究提供了居住在淡水抽水环境（Baetis，Hydropsyche，Rhyacophila和Onychogomphus）中的四种大型无脊椎动物无脊椎动物的能量密度（ED）数据和模型。样品是在弗留利·威尼斯·朱利亚地区不同水道类型（溪流，河流，河道和沟渠）内的水文淡水网络中收集的，并使用绝热炸弹量热计直接测量了能量密度。在所有研究属中，以焦耳克^-1湿重表示的测得的ED与干重百分率（DW％）呈正相关（r ² > 0.9504），从而可以基于ED和DW％之间的关系获得特定属的预测模型。模型经过验证并具有良好的预测能力，因为观察到的百分比误差的第90个百分位数在4.23％和5.18％之间，而中位数在1.32％和2.83％之间。ANCOVA揭示了模型之间的显着差异，例如针对Rhyacophila和Onychogomphus的模型与其他人明显不同。经验模型用于建立估计能量密度的数据集，以评估能量密度与受监测河流系统生态状况之间的关系，并通过应用四个不同的生态指数（ICMi，RQE_IBMR ，STAR_ICMi和LIMeco）。在生物监测计划中仍然忽略了淡水大型底栖无脊椎动物的ED含量信息，但在生态状况评估的背景下，特别是与被调查河流系统的营养状况相关的某些生态状况的解释可能是有用的。