Aquatic biodiversity enhances multiple nutritional benefits to humans

AbstractHumanity depends on biodiversity for health, well-being and a stable environment. As biodiversity change accelerates, we are still discovering the full range of consequences for human health and well-being. Here, we test the hypothesis -- derived from biodiversity - ecosystem functioning theory -- that species richness and ecological functional diversity allow seafood diets to fulfill multiple nutritional requirements, a condition necessary for human health. We analyzed a newly synthesized dataset of 7245 observations of nutrient and contaminant concentrations in 801 aquatic animal taxa, and found that species with different ecological traits have distinct and complementary micronutrient profiles, but little difference in protein content. The same complementarity mechanisms that generate positive biodiversity effects on ecosystem functioning in terrestrial ecosystems also operate in seafood assemblages, allowing more diverse diets to yield increased nutritional benefits independent of total biomass consumed. Notably, nutritional metrics that capture multiple micronutrients essential for human well-being depend more strongly on biodiversity than common ecological measures of function such as productivity, typically reported for grasslands and forests. Further, we found that increasing species richness did not increase the amount of protein in seafood diets, and also increased concentrations of toxic metal contaminants in the diet. Seafood-derived micronutrients are important for human health and are a pillar of global food and nutrition security. By drawing upon biodiversity-ecosystem functioning theory, we demonstrate that ecological concepts of biodiversity can deepen our understanding of nature’s benefits to people and unite sustainability goals for biodiversity and human well-being., MethodsData were collected by extracting data from previously published articles. Please see Bernhardt and O'Connor 2021, PNAS for detailed Methods., Usage notesMetadata for Bernhardt and O'Connor 2021 - North American seafood contaminant dataset (\"seafood-contaminant-data-cleaned.csv\") obs_id: unique identifier for each contaminant observation taxon_name: genus and species, checked against one of the following taxonomic data sources: Catalogue of Life, Encyclopedia of Life, GBIF Backbone Taxonomy, World Register of Marine Species, OBIS genus_species: genus and species extracted from original data sources subgroup: broad taxonomic group: finfish / crustacean / mollusc taxon_common_name: species common name in English location_of_study: location of sample collection methylmercury: concentration of methylmercury in ug / 100g raw edible tissue. Methylmercury was calculated from total mercury following Sunderland et al. 2007 (assuming methylmercury = 95% of total mercury in finfish and crustaceans, 30% of total mercury in molluscs) lead: concentration of total lead in ug / 100g raw edible tissue. arsenic: concentration of total arsenic in ug / 100g raw edible tissue. cadmium: concentration of total cadmium in ug / 100g raw edible tissue. part: body part included in the edible portion. dataset: if the observation comes from an existing data compilation, first author of original data compilation. bibliography: complete reference information for observation. number_of_samples: if applicable, the number of individuals or individual samples included in contaminant concentration estimation. Metadata for Bernhardt and O'Connor 2021 - Global seafood nutrient dataset (\"global-seafood-nutrient-dataset-raw.csv\") All nutrient concentrations are presented per 100 g edible portion on a fresh weight basis, for only raw or frozen samples (no prepared seafood items). obs_id: unique identifier for each sample in the dataset. taxon_name: scientific name (genus and species), as resolved with one of the following taxonomic data sources: Catalogue of Life, Encyclopedia of Life, GBIF Backbone Taxonomy, World Register of Marine Species, OBIS common_name: common name in English subgroup: broad taxonomic group; mollusc, crustacean, finfish sample_info: sample description, including information on which parts are included body_part: for finfish samples, body part in the edible portion sample; muscle (fillet or skinless fillet), muscle and organs (including bones, liver, etc), eggs, liver, oil ca_mg: concentration of calcium in edible portion, in mg / 100g edible portion (includes both elemental and ionic forms) fe_mg: concentration of iron in edible portion, in mg / 100g edible portion (includes both elemental and ionic forms) zn_mg: concentration of zinc in edible portion, in mg / 100g edible portion (includes both elemental and ionic forms) epa: concentration of EPA in edible portion, in g / 100g edible portion dha: concentration of DHA in edible portion, in g / 100g edible portion fat: concentration of fat in edible portion, in g / 100g edible portion (total fat; following INFOODS, calculated as one of the following: sum of triglycerides, phospholipids, sterols and related compounds; derived by analysis using continuous extraction, method of determination unknown or mixed methods) protein: concentration of protein in edible portion, in g / 100g edible portion (total protein; following INFOODS, calculated by any of the following methods: calculated from total nitrogen, calculated from protein nitrogen, method of determination unknown or variable) location: location of sample collection season: season of sample collection biblio_id: unique identifier for each reference bibliography: complete reference information for original data source comments_on_data_processing_methods: notes describing how data were processed relative to their original source publication_year: year of publication of original data source food_item_id: if observation comes from an existing compilation, the id of that observation in pre-existing database caldata: 0 or 1, corresponding to whether the sample has a calcium measurement irondata: 0 or 1, corresponding to whether the sample has an iron measurement zincdata: 0 or 1, corresponding to whether the sample has a zinc measurement epadata: 0 or 1, corresponding to whether the sample has an EPA measurement dhadata: 0 or 1, corresponding to whether the sample has a DHA measurement proteindata: 0 or 1, corresponding to whether the sample has a protein measurement fatdata: 0 or 1, corresponding to whether the sample has a fat measurement data_here: total number of nutrients for which there are data for the sample## 摘要 人类的健康、福祉与稳定的生存环境均依赖生物多样性（biodiversity）。随着生物多样性变化速率加快，我们仍在全面探究其对人类健康与福祉的各类影响。本研究旨在验证源自生物多样性-生态系统功能理论（biodiversity-ecosystem functioning theory）的假说：物种丰富度与生态功能多样性可使海鲜膳食满足多项营养需求，而这是保障人类健康的必要条件。 我们对全新整合的数据集展开分析，该数据集涵盖801个水生动物类群（aquatic animal taxa）的7245项营养与污染物浓度观测记录，结果发现：具备不同生态特征的物种，其微量营养素谱存在显著差异且相互补充，但蛋白质含量差异极小。 在陆地生态系统中驱动生物多样性对生态系统功能产生正向影响的同一类互补机制，同样存在于海鲜类群组合中，这使得更多样化的膳食能够在不依赖总摄入生物量的前提下，带来更多营养益处。 值得注意的是，相较于通常在草原与森林生态系统中报道的常见生态功能测量指标（如生产力），能够反映多种人类福祉必需微量营养素的营养指标，对生物多样性的依赖性更强。 此外，我们发现：海鲜膳食中的物种丰富度提升并不会增加蛋白质含量，但会提高膳食中有毒金属污染物的浓度。 海鲜来源的微量营养素对人类健康至关重要，是全球粮食与营养安全的重要支柱。本研究借助生物多样性-生态系统功能理论，证明了生物多样性的生态学概念能够加深我们对自然给人类带来的益处的理解，并可将生物多样性与人类福祉的可持续发展目标相结合。 ## 研究方法 研究数据通过提取已发表文献中的数据获得，详细实验方法请参阅Bernhardt与O'Connor 2021年发表于《美国国家科学院院刊》（PNAS）的论文。 ## 使用说明 ### 北美海鲜污染物数据集（Bernhardt和O'Connor 2021）——元数据说明（文件：`seafood-contaminant-data-cleaned.csv`） - obs_id：每项污染物观测记录的唯一标识符 - taxon_name：属与种名称，已通过以下分类学数据源之一进行校验：生命目录（Catalogue of Life）、生命百科全书（Encyclopedia of Life）、全球生物多样性信息机构骨干分类法（GBIF Backbone Taxonomy）、世界海洋物种登记册（World Register of Marine Species）、海洋生物普查数据库（OBIS） - genus_species：从原始数据源提取的属与种名称 - subgroup：广义分类群：鱼类（finfish）/甲壳类（crustacean）/软体动物（mollusc） - taxon_common_name：物种英文通用名 - location_of_study：样本采集地点 - methylmercury：甲基汞（methylmercury）浓度，单位为μg/100g可食用生组织。甲基汞浓度参照Sunderland等人2007年的方法由总汞浓度推算得出（假设鱼类与甲壳类体内甲基汞占总汞的95%，软体动物体内占30%） - lead：总铅浓度，单位为μg/100g可食用生组织 - arsenic：总砷浓度，单位为μg/100g可食用生组织 - cadmium：总镉浓度，单位为μg/100g可食用生组织 - part：可食用部分对应的身体部位 - dataset：若观测记录来自已有的数据汇编，则为该汇编的第一作者 - bibliography：该观测记录的完整参考文献信息 - number_of_samples：若适用，指用于污染物浓度估算的个体或样本数量 ### 全球海鲜营养数据集（Bernhardt和O'Connor 2021）——元数据说明（文件：`global-seafood-nutrient-dataset-raw.csv`） 所有营养物质浓度均以鲜重计，单位为每100g可食用部分，且仅涵盖生制或冷冻样本（不包含加工海鲜制品）。 - obs_id：数据集中每个样本的唯一标识符 - taxon_name：物种学名（属与种），已通过以下分类学数据源之一完成校验：生命目录（Catalogue of Life）、生命百科全书（Encyclopedia of Life）、全球生物多样性信息机构骨干分类法（GBIF Backbone Taxonomy）、世界海洋物种登记册（World Register of Marine Species）、海洋生物普查数据库（OBIS） - common_name：物种英文通用名 - subgroup：广义分类群；鱼类（finfish）、甲壳类（crustacean）、软体动物（mollusc） - sample_info：样本描述，包含所涵盖的身体部位信息 - body_part：针对鱼类样本，指可食用部分对应的身体部位；可选值包括：肌肉（鱼排或去皮鱼排）、肌肉与内脏（含骨骼、肝脏等）、鱼卵、肝脏、鱼油 - ca_mg：可食用部分的钙浓度，单位为mg/100g可食用部分（涵盖元素态与离子态钙） - fe_mg：可食用部分的铁浓度，单位为mg/100g可食用部分（涵盖元素态与离子态铁） - zn_mg：可食用部分的锌浓度，单位为mg/100g可食用部分（涵盖元素态与离子态锌） - epa：二十碳五烯酸（EPA）浓度，单位为g/100g可食用部分 - dha：二十二碳六烯酸（DHA）浓度，单位为g/100g可食用部分 - fat：可食用部分的脂肪浓度，单位为g/100g可食用部分（总脂肪；遵循国际食品数据系统网络（INFOODS）标准，计算方式为以下之一：甘油三酯、磷脂、甾醇及相关化合物的总和；通过连续萃取法分析得出；测定方法未知或采用混合方法） - protein：可食用部分的蛋白质浓度，单位为g/100g可食用部分（总蛋白质；遵循国际食品数据系统网络（INFOODS）标准，计算方式为以下之一：由总氮推算得出、由蛋白质氮推算得出、测定方法未知或存在差异） - location：样本采集地点 - season：样本采集季节 - biblio_id：每条参考文献的唯一标识符 - bibliography：原始数据源的完整参考文献信息 - comments_on_data_processing_methods：描述数据相对于原始数据源的处理方式的说明 - publication_year：原始数据源的发表年份 - food_item_id：若观测记录来自已有的数据汇编，则为该观测记录在预先生成数据库中的ID - caldata：0或1，用于标识该样本是否有钙含量测量数据 - irondata：0或1，用于标识该样本是否有铁含量测量数据 - zincdata：0或1，用于标识该样本是否有锌含量测量数据 - epadata：0或1，用于标识该样本是否有EPA含量测量数据 - dhadata：0或1，用于标识该样本是否有DHA含量测量数据 - proteindata：0或1，用于标识该样本是否有蛋白质含量测量数据 - fatdata：0或1，用于标识该样本是否有脂肪含量测量数据 - data_here：该样本具备测量数据的营养物质总数量