Data From: Percent ash-free dry weight as a robust method to estimate energy density across taxa

Determining how energy flows through ecosystems reveals underlying ecological patterns that drive processes such as growth and food web dynamics. Models that assess the transfer of energy from producers to consumers require information on the energy content or energy density (ED) of prey species. ED is most accurately measured through bomb calorimetry, but this method suffers from limitations of cost, time and sample requirements that often make it unrealistic for many studies. Percent dry-weight (DW) is typically used as a proxy for ED, but this measure includes an indigestible portion (e.g. bones, shell, salt) that can vary widely among organisms. Further, several distinct models exist for various taxonomic groups, yet none can accurately estimate invertebrate, vertebrate and plant ED with a single equation. Here, we present a novel method to estimate the ED of organisms using percent ash-free dry weight (AFDW). Using data obtained from 11 studies diverse in geographic, temporal and taxonomic scope, AFDW, DW as well as percent-protein and percent-lipid were compared as predictors of ED. Linear models were produced on a logarithmic scale, including dummy variables for broad taxonomic groups. AFDW was the superior predictor of ED compared to DW, percent-protein and percent-lipid content. Model selection revealed that using correction factors (dummy variables) for aquatic animals (AA) and terrestrial invertebrates (TI) produced the best supported model – log10(ED) = 1.07*log10(AFDW) – 0.80 (R2 = 0.978, p<0.00001) – with an intercept adjustment of 0.09 and 0.04 for AA and TI, respectively. All models including AFDW as a predictor had high predictive power (R2>0.97), suggesting that AFDW can be used with high degrees of certainty to predict the ED of taxonomically diverse organisms. Our AFDW model will allow ED to be measured with minimal cost and time requirements and excludes ash-weight from estimates of digestible mass. Its ease of use will allow for ED to be more readily and accurately determined for diverse taxa across different ecosystems. This data file includes the original values and sources used to create the AFDW model.解析生态系统内的能量流动路径，能够揭示驱动生物生长与食物网动态等过程的底层生态模式。用于评估能量从生产者到消费者传递过程的模型，需要获取猎物物种的能量含量或能量密度（energy density, ED）数据。目前能量密度最精准的测量方式为弹式量热法（bomb calorimetry），但该方法存在成本高昂、耗时较长且对样本量要求严苛等局限，致使多数研究难以实际应用。研究中通常采用干重百分比（dry-weight, DW）作为能量密度的替代指标，但该指标包含了生物体内不可消化的组分（如骨骼、外壳、盐分等），且不同生物间这类组分的占比差异显著。此外，虽已有针对不同分类群（taxonomic groups）的多款专属模型，但尚无一款单一方程可精准估算无脊椎动物、脊椎动物以及植物的能量密度。本研究提出一种全新的方法，通过无灰干重百分比（ash-free dry weight, AFDW）来估算生物的能量密度。本研究整合了覆盖地理、时间与分类学范围均较为广泛的11项研究的数据，将无灰干重百分比、干重百分比、蛋白质百分比以及脂质百分比作为能量密度的预测因子进行对比分析。研究构建了对数尺度下的线性模型，并纳入针对宽泛分类群的虚拟变量（dummy variables）。相较于干重百分比、蛋白质百分比与脂质百分比，无灰干重百分比是预测能量密度的最优因子。模型选择结果显示，为水生动物（aquatic animals, AA）与陆生无脊椎动物（terrestrial invertebrates, TI）设置校正因子（correction factors，即虚拟变量）时，可得到拟合效果最佳的模型：log10(ED) = 1.07*log10(AFDW) - 0.80（决定系数R²=0.978，p<0.00001），其中水生动物与陆生无脊椎动物的截距校正值分别为0.09与0.04。所有纳入无灰干重百分比作为预测因子的模型均具备极高的预测能力（决定系数R²>0.97），这表明无灰干重百分比可被高度可靠地用于预测不同分类学类群生物的能量密度。本研究提出的无灰干重百分比模型，可在大幅降低时间与成本投入的前提下实现能量密度的测定，且估算可消化物质时无需纳入灰分重量。该模型操作简便，能够更便捷且精准地测定不同生态系统中多样分类群生物的能量密度。本数据集文件包含用于构建无灰干重百分比模型的原始数值与相关文献来源。