Predator-prey diet linkages with error range for the Gulf of Mexico fitted using maximum likelihood method.

Data is from Masi, M and Ainsworth, C. (in press) A Probabilistic Representation of Fish Diet Compositions from Multiple Data Sources: A Gulf of Mexico Case Study. Ecological Modelling. April 2014. Trophic ecosystem models are interactive tools that allow decision makers to analyze how a management decision can impact an ecosystem on a multi-species level, and are increasingly being used as a supplement to the current single species approach to fisheries management. The functionality of such a model is dependent upon an accurate representation of the trophic interactions occurring within a study area. Typical methods for developing a diet matrix to be used in ecosystem models often fail to account for uncertainty associated with sampling; this is especially relevant when dealing with small diet data sets. In this case study of the Gulf of Mexico ecosystem, we have conducted a laboratory diet analysis to define predator-prey interactions for non-commercially important predator species resident to the study area, and then expounded on this laboratory data by assimilating two, more robust data sets. By applying a maximum likelihood estimation method, we combine these data sets and produce maximum likelihood estimates (MLEs) and associated error ranges, which describe the likely diet contribution that a given prey item contributes to a predator’s diet. These results will be used to parameterize the availabilities (diet) matrix of an Atlantis ecosystem model of the Gulf of Mexico. Column A: predator name. Column B: prey name. Column C: lower 95% confidence interval. Column D: upper 95% confidence interval. Column E: mode of the maximum likelihood marginal beta distribution (percent).本数据集源自Masi、M与Ainsworth、C的已录用待刊论文《基于多数据源的鱼类食性组成概率表征：以墨西哥湾为例》，刊载于《生态建模》，2014年4月。 营养级生态系统模型是一类交互式工具，可帮助决策者从多物种维度分析管理决策对生态系统的影响，当前其正日益被用作单一物种种群渔业管理方案的补充手段。 此类模型的功能有效性，取决于研究区域内营养级相互作用的精准表征。 常规用于构建生态系统模型食性矩阵的方法，往往未考虑采样相关的不确定性；在处理小型食性数据集时，这一问题尤为突出。 在本次墨西哥湾生态系统案例研究中，研究团队首先开展实验室食性分析，以明确研究区域内非经济捕食鱼种的捕食-猎物相互作用关系；随后通过整合两组更为完善的数据集，对该实验室数据进行补充拓展。 本研究通过应用最大似然估计（maximum likelihood estimation, MLE）方法，对上述数据集进行整合，得到最大似然估计值及其对应的误差区间，以此表征特定猎物在捕食者食性中的潜在贡献占比。 本研究所得结果将用于参数化墨西哥湾Atlantis生态系统模型的资源可获得性（食性）矩阵。 列A：捕食者名称；列B：猎物名称；列C：95%置信区间下限；列D：95%置信区间上限；列E：最大似然边缘Beta分布的众数（百分比）。