Length-mass equations for freshwater unionid mussel assemblages: Implications for estimating ecosystem function
Length-mass equations for freshwater unionid mussel assemblages: Implications for estimating ecosystem function
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DOI：
10.1086/708950
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发表时间：
2020-09-01
影响因子：
1.8
通讯作者：
Haag, Wendell R.
中科院分区：
文献类型：
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作者：
Atkinson, Carla L.;Parr, Thomas B.;Haag, Wendell R.
生物量通常用于衡量个体及其功能特性对社区或生态系统的贡献。然而，准确的生物量测量可能需要破坏性取样，这对蚌类等长寿生物体是有害的。我们积累了6684测量的长度和软组织干质量（STDM）或壳干质量（SHDM）从43种蚌类的数据库，以减少破坏性取样的需要。我们使用这些数据产生回归方程，涉及最大壳长质量（无论是STDM或SHDM）在3个分类水平：家庭，系统发育部落，和物种。对广布的蚌类Amblemaplicata和Elliptioplatanata，建立了6个水体和流域的体长-STDM回归方程。我们还使用了自举rescue在家庭层面上开发一个通用的回归方程unionid贻贝。我们比较了所有3个分类水平内的模型，以确定乘法（对数-对数变换）或加法（非线性参数估计）误差结构是否更适合生物量预测。对于83%的部落（n= 5，averager（2）= 0.95 +/- 0.03）和90%的物种（n= 33，averager（2）= 0.93 +/-0.07），具有乘法误差的模型最适合科水平的长度-STDM数据（STDM = 6.63 x 10（-6）xL（max）（2.89）x 1.13;r（2）= 0.94）。对于长度SHDM，具有乘法误差的模型最适合家族水平（SHDM = 2.98 x 10（-4）xL（max）（2.98）x 1.32;r（2）= 0.86）、所有部落（n= 5，平均值（2）= 0.88 +/- 0.13）和所有物种（n= 27，平均值（2）= 0.94 +/- 0.09）。具有乘法误差的模型也为我们的两个广泛分布的物种A提供了最佳拟合（r（2）> 0.76）。褶纹和E.扁平的最后，我们提出了一个案例研究的基础上收集的数据，从19个河流站点在亚拉巴马和俄克拉荷马州，美国，以确定我们的权力关系的性能（自举响应与长度STDM回归）。在这两个河流系统，部落和物种的特定方程提高了2- 20%的家庭水平回归unionid STDM的预测。精细的分类分辨率方程产生更准确的质量预测，但缺乏准确的分类鉴定，我们的家庭级回归STDM将产生可接受的估计，这是关键参数时，估计贻贝的生态系统服务的贡献。我们的研究提供了一个工具包，使科学家和管理人员能够非破坏性地量化淡水蚌类的生物量（具有不确定性），用于二次生产，生态系统功能和服务估计。
Biomass is often used to scale the contributions of individuals and their functional traits to a community or ecosystem. However, accurate biomass measurements can require destructive sampling, which is detrimental to long-lived organisms such as unionid mussels. We amassed a database of 6684 measurements of length and soft tissue dry mass (STDM) or shell dry mass (SHDM) from 43 species of unionid mussels to reduce the need for destructive sampling. We used these data to produce regression equations that relate maximum shell length to mass (either STDM or SHDM) at 3 taxonomic levels: family, phylogenetic tribe, and species. For 2 widely-distributed unionid species,Amblema plicataandElliptio complanata, we present length-STDM regression equations from 6 waterbodies and basins. We also used bootstrapping resampling at the family level to develop a universal regression equation for unionid mussels. We compared models within all 3 taxonomic levels to determine if multiplicative (log-log transformation) or additive (non-linear parameter estimation) error structures provided better fits for biomass prediction. Models with multiplicative errors best fit length-STDM data at the family level (STDM = 6.63 x 10(-6)xL(max)(2.89)x 1.13;r(2)= 0.94), for 83% of tribes (n= 5, averager(2)= 0.95 +/- 0.03), and for 90% of species (n= 33, averager(2)= 0.93 +/- 0.07). For length-SHDM, models with multiplicative errors best fit the family level (SHDM = 2.98 x 10(-4)xL(max)(2.98)x 1.32;r(2)= 0.86), all tribes (n= 5, averager(2)= 0.88 +/- 0.13), and all species (n= 27, averager(2)= 0.94 +/- 0.09). Models with multiplicative errors also provided the best fit (r(2)> 0.76) for our 2 wide-ranging species,A. plicataandE. complanata. Finally, we present a case study based on data collected from 19 river sites in Alabama and Oklahoma, USA, to determine the performance of our power relationships (bootstrap resampling versus length-STDM regressions). In both river systems, tribe- and species-specific equations improved the prediction of unionid STDM over the family-level regression by 2-20%. Finer taxonomic resolution equations produce more accurate mass predictions, but where accurate taxonomic identifications are lacking, our family-level regression for STDM will produce acceptable estimates, which are key parameters when estimating mussel contributions to ecosystem services. Our study provides a toolkit that will allow scientists and managers to non-destructively quantify biomass (with uncertainty) of freshwater unionid mussels for secondary production, ecosystem function, and services estimates.