Multispecies models are widely used to evaluate management trade‐offs arising from species interactions. However, identifying climate impacts and sensitive habitats requires integrating spatial heterogeneity and environmental impacts into multispecies models at fine spatial scales. We therefore develop a spatio‐temporal model of intermediate complexity for ecosystem assessments (a “MICE‐in‐space”), which is fitted to survey sampling data and time series of fishing mortality using maximum‐likelihood techniques. The model is implemented in the VAST R package, and it can be configured to range from purely descriptive to including ratio‐dependent interactions among species. We demonstrate this model using data for four groundfishes in the Gulf of Alaska using data from 1982 to 2015. Model selection for this case‐study shows that models with species interactions are parsimonious, although a model specifying separate density dependence without interactions also has substantial support. The AIC‐selected model estimates a significant, negative impact of Alaska pollock (Gadus chalcogrammus, Gadidae) on productivity of other species and suggests that recent fishing mortality for Pacific cod (G. microcephalus, Gadidae) is above the biological reference point (BRP) resulting in 40% of unfished biomass; other models show similar trends but different scales due to different BRP estimates. A simulation experiment shows that fitting a model with fewer species at a coarse spatial resolution degrades estimation performance, but that interactions and biological reference points can still be estimated accurately. We conclude that MICE‐in‐space models can simultaneously estimate fishing impacts, species trade‐offs, biological reference points and habitat quality. They are therefore suitable to forecast short‐term climate impacts, optimize survey designs and designate protected habitats.

中文翻译：

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生态系统评估中度复杂性的时空模型：空间渔业管理的新工具

多物种模型被广泛用于评估物种相互作用引起的管理权衡。但是，要确定气候影响和敏感的栖息地，就需要在精细的空间尺度上将空间异质性和环境影响整合到多物种模型中。因此，我们开发了一个用于生态系统评估的中间复杂度的时空模型（“ MICE-in-space”），该模型适用于使用最大似然技术来调查抽样数据和捕捞死亡率的时间序列。该模型在VAST中实现R包，它的配置范围从纯粹的描述性到物种间比率依赖的相互作用。我们使用1982年至2015年的数据，使用阿拉斯加湾的四个底栖鱼类的数据演示了该模型。此案例研究的模型选择显示，具有物种相互作用的模型是简约的，尽管指定了单独的密度依赖性而没有相互作用的模型也有很大的支持。 。AIC选择的模型估计了阿拉斯加狭鳕（Gadus chalcogrammus，Gadidae）对其他物种生产力的显着负面影响，并表明太平洋鳕（G. microcephalus）的近期捕捞死亡率（Gadidae）高于生物参考点（BRP），导致40％的未捕捞生物量；其他模型显示出相似的趋势，但由于BRP估算值不同，因此规模不同。仿真实验表明，在粗糙的空间分辨率下以较少的物种拟合模型会降低估计性能，但仍可以准确估计相互作用和生物学参考点。我们得出的结论是，MICE的空间模型可以同时估算捕捞影响，物种权衡，生物参考点和栖息地质量。因此，它们适合于预测短期气候影响，优化调查设计并指定受保护的栖息地。