The global fish and invertebrate abundance value of mangroves dataset

This dataset is the species and species group predictions of the density of 37 commercially important fish and invertebrates that are known to extensively use mangroves. All methods are provided in detail in the accompanying bioRxiv preprint, zu Ermgassen et al. (2024) The global fish and invertebrate abundance value of mangroves Description of files Mangrove_commercial_fauna_density_data_references.csv: this is the raw data used to create the linear model using generalized least squares relating the fish density values to the covariate data R Scripts & Data This folder contain several R scripts and data files to used calculate the values in zu Ermgassen et al. (2024) The global fish and invertebrate abundance value of mangroves. Species Predictions* all_sp_fit_fn.csv: the mean predicted species density for 37 commercially important fish and invertebrates for a grid with a spatial resolution of 1 km2. all_sp_fit_fn_lower.csv: the lower (1.96 * standard error of the model fit) predicted species density for 37 commercially important fish and invertebrates for a grid with a spatial resolution of 1 km2. all_sp_fit_fn_upper.csv: the upper (1.96 * standard error of the model fit) predicted species density for 37 commercially important fish and invertebrates for a grid with a spatial resolution of 1 km2. Species Name Contractions.csv: file with key to name contractions in above datasets Shapfiles: spatial representations of the above datasets based on the 1km2 grid Species Group Predictions all_sp_fit_fn_total.csv: the mean, lower and upper (1.96 * standard error of the model fit) predicted species density for 37 commercially important fish and invertebrates for a grid with a spatial resolution of 1 km2, with species summed into finfishes (n = 29), crabs (n = 4), bivalves (n = 1), and prawns (n = 3). Shapfiles: spatial representations of the above dataset based on the 1km2 grid * N.B. data labeled Neosarmatium meinerti in the above files has been corrected to Neosarmatium africanum

本数据集针对37种广泛栖息利用红树林的具有商业开发价值的鱼类及无脊椎动物，提供其物种及物种类群的密度预测值。所有研究方法已在随附的bioRxiv预印本《zu Ermgassen等人（2024）：红树林全球鱼类与无脊椎动物丰度价值》中详细阐释。

### 数据文件说明
Mangrove_commercial_fauna_density_data_references.csv：本文件为构建广义最小二乘法（generalized least squares）线性模型的原始数据，该模型将鱼类密度值与协变量数据进行关联。

#### R脚本与数据文件夹
本文件夹包含多个R脚本及数据文件，用于复现《zu Ermgassen等人（2024）：红树林全球鱼类与无脊椎动物丰度价值》一文中的相关计算结果。

#### 物种预测（Species Predictions）
- all_sp_fit_fn.csv：针对空间分辨率为1 km²的网格，包含37种商业性鱼类和无脊椎动物的平均预测物种密度。
- all_sp_fit_fn_lower.csv：针对空间分辨率为1 km²的网格，包含37种商业性鱼类和无脊椎动物的预测物种密度下限值（模型拟合标准误的1.96倍）。
- all_sp_fit_fn_upper.csv：针对空间分辨率为1 km²的网格，包含37种商业性鱼类和无脊椎动物的预测物种密度上限值（模型拟合标准误的1.96倍）。
- Species Name Contractions.csv：本文件为上述数据集中物种名称缩写的对照表。
- 形状文件（Shapfiles）：基于1 km²网格的上述数据集的空间可视化文件。

#### 物种类群预测（Species Group Predictions）
- all_sp_fit_fn_total.csv：针对空间分辨率为1 km²的网格，包含37种商业性鱼类和无脊椎动物的预测物种密度均值、下限与上限值（模型拟合标准误的1.96倍），其中物种已被汇总为硬骨鱼类（29种）、蟹类（4种）、双壳类（1种）及对虾类（3种）四大类群。
- 形状文件（Shapfiles）：基于1 km²网格的上述数据集的空间可视化文件。

* 注：上述文件中标记为Neosarmatium meinerti的数据已被更正为Neosarmatium africanum。