Evaluating the performance of a system model in predicting zooplankton dynamics: Insights from the Bering Sea ecosystem

IF 1.9 2区 农林科学 Q2 FISHERIES
G. Sullaway, Curry J. Cunningham, David G Kimmel, Darren J. Pilcher, James T Thorson
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引用次数: 0

Abstract

Understanding how ecosystem change influences fishery resources through trophic pathways is a key tenet of ecosystem‐based fishery management. System models (SM), which use numerical modeling to describe physical and biological processes, can advance inclusion of ecosystem and prey information in fisheries management; however, incorporating SMs in management requires evaluation against empirical data. The Bering Ecosystem Study Nutrient‐Phytoplankton‐Zooplankton (BESTNPZ) model is an SM (originally created by the Bering Ecosystem Study, which initiated in 2006 and was expanded by Kearney et al.) includes zooplankton biomass hindcasts for the Bering Sea. In the Bering Sea, zooplankton are an important prey item for fishery species, yet the zooplankton component of this SM has not been validated against empirical data. We compared empirical zooplankton data to BESTNPZ hindcast estimates for three zooplankton functional groups and found that the two sources of information are on different absolute scales. We found high correlation between relative seasonal biomass trends estimated by BESTNPZ and empirical data for large off‐shelf copepods (Neocalanus spp.) and low correlations for large on‐shelf copepods and small copepods (Calanus spp. and Pseudocalanus spp., respectively). To address these discrepancies, we constructed hybrid species distribution models (H‐SDM), which predict zooplankton biomass using the BESTNPZ hindcast and environmental covariates. We found that H‐SDMs offered marginal improvements over correlative species distribution models (C‐SDMs) relying solely on empirical data for spatial extrapolation and little improvement for most functional groups when forecasting short‐term temporal zooplankton biomass trends. Overall, we suggest that interpretation of current BESTNPZ hindcasts should be tempered by our understanding of key mismatches in absolute scale, seasonality, and annual indices between BESTNPZ and empirical data.
评估系统模型在预测浮游动物动态方面的性能:白令海生态系统的启示
了解生态系统变化如何通过营养途径影响渔业资源是基于生态系统的渔业管理的关键原则。系统模型(SM)使用数值建模来描述物理和生物过程,可推动将生态系统和猎物信息纳入渔业管理;然而,将系统模型纳入管理需要根据经验数据进行评估。白令生态系统研究营养-浮游植物-浮游动物(BESTNPZ)模型是一个 SM 模型(最初由白令生态系统研究创建,该研究始于 2006 年,后由 Kearney 等人扩展),包括白令海浮游动物生物量后报。在白令海,浮游动物是渔业物种的重要捕食对象,但该模式的浮游动物部分尚未根据经验数据进行验证。我们比较了浮游动物的经验数据和 BESTNPZ 对三个浮游动物功能群的后报估计值,发现这两种信息来源的绝对尺度不同。我们发现,BESTNPZ 估算的相对季节性生物量趋势与大型离岸桡足类(Neocalanus 属)的经验数据之间具有很高的相关性,而大型上岸桡足类和小型桡足类(分别为 Calanus 属和 Pseudocalanus 属)的相关性较低。为了解决这些差异,我们构建了混合物种分布模型(H-SDM),利用 BESTNPZ 后报和环境协变量预测浮游动物的生物量。我们发现,与仅依靠经验数据进行空间外推的相关物种分布模型(C-SDMs)相比,H-SDMs 在预测浮游动物生物量的短期时间趋势时,对大多数功能群的改进不大。总之,我们建议,在解释目前的 BESTNPZ 后期预报时,应了解 BESTNPZ 与经验数据在绝对尺度、季节性和年指数方面的主要不匹配之处。
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来源期刊
Fisheries Oceanography
Fisheries Oceanography 农林科学-海洋学
CiteScore
5.00
自引率
7.70%
发文量
50
审稿时长
>18 weeks
期刊介绍: The international journal of the Japanese Society for Fisheries Oceanography, Fisheries Oceanography is designed to present a forum for the exchange of information amongst fisheries scientists worldwide. Fisheries Oceanography: presents original research articles relating the production and dynamics of fish populations to the marine environment examines entire food chains - not just single species identifies mechanisms controlling abundance explores factors affecting the recruitment and abundance of fish species and all higher marine tropic levels
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