Predicted distribution of seagrass communities across the Great Barrier Reef World Heritage Area and adjacent estuaries (NESP TWQ Project 5.4, TropWATER, James Cook University)

This dataset describes the predicted distribution of seagrass communities across the Great Barrier Reef World Heritage Area and adjacent estuaries, based on six multivariate regressions tree models for estuary intertidal, estuary subtidal, coastal intertidal, coastal subtidal, reef intertidal, and reef subtidal. The models are presented as six raster datasets with 30m resolution.Managing seagrass resources in the GBRWHA requires adequate information on the spatial extent of seagrass communities. The enormous size of the GBRWHA (1000s of kilometres) and the remoteness of many seagrass meadows from human populations means that models are a useful tool to predict where different seagrass communities are likely to be in areas where data is lacking. James Cook University’s Centre for Tropical Water & Aquatic Ecosystem Research (TropWATER) has been collecting spatial data on GBR seagrass since the early 1980s. This project used TropWATER’s synthesis of seagrass site data (NESP Project 3.1 and 5.4: https://eatlas.org.au/data/uuid/5011393e-0db7-46ce-a8ee-f331fcf83a88) to predict seagrass communities. In making this data publically available for management, the authors from the TropWATER Seagrass Group request being contacted and involved in decision making processes that incorporate this data, to ensure its limitations are fully understood.Methods:Seagrass dataThe sampling methods used to study, describe and monitors seagrass meadows were developed by the TropWATER Seagrass Group and tailored to the location and habitat surveyed; descriptions and references are available in the metadata for the GBRWHA data composite (https://eatlas.org.au/data/uuid/5011393e-0db7-46ce-a8ee-f331fcf83a88).Environmental dataEnvironmental predictors used in the models were: depth below mean sea level (Beaman 2017), relative tidal exposure (Bishop-Taylor et al. 2019), water type (Marine Water Bodies definitions version 2_4, Data courtesy of the Great Barrier Reef Marine Park Authority; Dyall et al. 2004), proportion mud in the sediment (coast and reef models, https://research.csiro.au/ereefs/models/model-outputs/access-to-raw-model-output/) (see also Baird et al. 2020; Margvelashvili et al. 2018), dominant sediment (estuary models only; https://eatlas.org.au/data/uuid/5011393e-0db7-46ce-a8ee-f331fcf83a88), benthic geomorphology (Heap and Harris 2008), benthic light https://dapds00.nci.org.au/thredds/catalog/fx3/gbr1_bgc_924/catalog.html (see also Baird et al. 2016; Baird et al. 2020), water temperature, mean current speed and salinity https://thredds.ereefs.aims.gov.au/thredds/catalog/ereefs/gbr1_2.0/all-one/catalog.html (Steven et al. 2019), wind speed (https://thredds.ereefs.aims.gov.au/thredds/catalog/ereefs/gbr1_2.0/all-one/catalog.html ) and Australian Bureau of Meteorology’s ACCESS data products (Bureau of Meteorology 2020; Soldatenko et al. 2018; Steven et al. 2019), and latitude. Different models had different combinations of predictors after removing collinear variables and excluding variables that did not extend into an area. For example, estuary models only include depth, relative tidal exposure, dominant sediment, and latitude.ModelsWe modelled seagrass communities in six areas: Estuary Intertidal, Estuary Subtidal, Coastal Intertidal, Coastal Subtidal, Reef Intertidal and Reef Subtidal. For each area we used multivariate regression trees to examine changes in seagrass community type within the GBRWHA and adjacent estuaries, using a matrix of seagrass presence/absence site data for 12 seagrass species in the data set. Multivariate regression trees (MRTs) were implemented using the R package mvpart (De’ath 2004) (available in archive form on CRAN at https://cran.r-project.org) in R version 4.0.2 (R Core Team 2020). The map in Figure 1 was created using ArcGIS 10.8.A detailed description of the modelled communities can be found in the final report for the NESP TWQ Project 5.4 (currently in review).Spatial limitsSeagrass community types were modelled within potential seagrass habitat. Potential seagrass habitat was modelled by Carter et al. 2020 and is available on eAtlas here: https://eatlas.org.au/data/uuid/108ee868-4fb1-4e5f-ae57-5d65198384cc . The models do not extend north and south of the GBRWHA. The models extend across the continental shelf but exclude waters deeper than ~100m east of the shelf that were not surveyed for seagrass. Data were included when sites extended west of the GBRWHA boundary into coastal and estuarine water immediately adjacent.Data setsThe site data used in this model is available here: https://eatlas.org.au/data/uuid/5011393e-0db7-46ce-a8ee-f331fcf83a88)Further information can be found in the upcoming publications of the final report for the NESP TWQ Project 5.4.Limitations of the data:The site data used in these models extends back to the mid-1980s. Large parts of the coast have not been mapped for seagrass presence since that time. The seagrass community rasters are at 30m grid resolution, however some environmental variables such as those from eReefs (wind speed, current speed, benthic light, water temperature) are from spatial data at 1km grid resolution, and are likely to vary at much smaller spatial scales that we could not include in these models. Format:This dataset consists of six raster datasets with a geographic coordinate system of WGS84. The rasters have been saved as layer packages with symbology representing seagrass communities. These are:Estuary intertidal communities: GBR_seagrass_communities_estuary_intertidal.lpkEstuary subtidal communities: GBR_seagrass_communities_estuary_subtidal.lpkCoastal intertidal communities: GBR_seagrass_communities_coastal_intertidal.lpkCoastal subtidal communities: GBR_seagrass_communities_coastal_subtidal.lpkReef intertidal communities: GBR_seagrass_communities_reef_intertidal.lpkReef subtidal communities: GBR_seagrass_communities_reef_subtidal.lpkReferences:TBCData Location:This dataset is filed in the eAtlas enduring data repository at: data\custodian\2019-2022-NESP-TWQ-5\5.4_Seagrass-Burdekin-regionAdditional licensing information:TropWATER gives no warranty in relation to the data (including accuracy, reliability, completeness, currency or suitability) and accepts no liability (including without limitation, liability in negligence) for any loss, damage or costs (including consequential damage) relating to any use of the data. TropWATER reserves the right to update, modify or correct the data at any time. The limitations of some older data included need to be understood and recognised. The TropWATER Seagrass Group would appreciate the opportunity to review documents providing research, management, legislative or compliance advice based on this data.

本数据集基于针对河口潮间带、河口潮下带、海岸潮间带、海岸潮下带、礁体潮间带及礁体潮下带的6套多元回归树（multivariate regression trees）模型，刻画了大堡礁世界遗产区域（Great Barrier Reef World Heritage Area, GBRWHA）及邻近河口的海草群落预测分布。本数据集包含6套分辨率为30米的栅格数据集。

对大堡礁世界遗产区域的海草资源进行管理，需要掌握海草群落空间分布范围的详实信息。大堡礁世界遗产区域幅员辽阔（跨度达数千公里），且多数海草床远离人类聚居区，因此在数据匮乏区域预测不同海草群落的潜在分布时，模型是极具价值的工具。

詹姆斯·库克大学热带水与水生生态系统研究中心（TropWATER）自20世纪80年代初起，便持续收集大堡礁海草的空间数据。本项目依托TropWATER整合的海草样点数据（国家环境科学项目（National Environmental Science Programme, NESP）3.1与5.4号项目：https://eatlas.org.au/data/uuid/5011393e-0db7-46ce-a8ee-f331fcf83a88）开展海草群落预测。为将本数据公开用于管理决策，TropWATER海草研究组的作者恳请在使用本数据的决策流程中联络并纳入该团队，以确保数据的局限性得到充分认知。

### 研究方法
1. 海草数据
用于研究、描述与监测海草床的采样方法由TropWATER海草研究组开发，并针对调查区域与生境类型进行了定制化调整；相关描述与参考文献可在大堡礁世界遗产区域复合数据的元数据中查阅（https://eatlas.org.au/data/uuid/5011393e-0db7-46ce-a8ee-f331fcf83a88）。

2. 环境数据
本模型使用的环境预测变量包括：平均海平面以下深度（Beaman 2017）、相对潮汐暴露度（Bishop-Taylor et al. 2019）、水体类型（海洋水体定义版本2_4，数据由大堡礁海洋公园管理局提供；Dyall et al. 2004）、沉积物泥粒占比（海岸与礁体模型，https://research.csiro.au/ereefs/models/model-outputs/access-to-raw-model-output/）（另见Baird et al. 2020; Margvelashvili et al. 2018）、优势沉积物类型（仅河口模型使用；https://eatlas.org.au/data/uuid/5011393e-0db7-46ce-a8ee-f331fcf83a88）、底栖地貌（Heap and Harris 2008）、底栖光照（https://dapds00.nci.org.au/thredds/catalog/fx3/gbr1_bgc_924/catalog.html，另见Baird et al. 2016; Baird et al. 2020）、水温、平均流速与盐度（https://thredds.ereefs.aims.gov.au/thredds/catalog/ereefs/gbr1_2.0/all-one/catalog.html，Steven et al. 2019）、风速（https://thredds.ereefs.aims.gov.au/thredds/catalog/ereefs/gbr1_2.0/all-one/catalog.html）以及澳大利亚气象局ACCESS数据产品（澳大利亚气象局2020; Soldatenko et al. 2018; Steven et al. 2019）与纬度。在去除共线性变量并排除无法覆盖研究区域的变量后，不同模型使用的预测变量组合各不相同。例如，河口模型仅包含深度、相对潮汐暴露度、优势沉积物类型与纬度。

3. 模型构建
我们针对6类区域建模海草群落：河口潮间带、河口潮下带、海岸潮间带、海岸潮下带、礁体潮间带与礁体潮下带。针对每类区域，我们采用多元回归树，基于数据集内12种海草物种的存在/缺失样点数据矩阵，分析大堡礁世界遗产区域及邻近河口内的海草群落类型变化。多元回归树（MRT）通过R语言mvpart包（De’ath 2004，可在CRAN存档页面https://cran.r-project.org获取）实现，运行环境为R 4.0.2版本（R Core Team 2020）。图1所示地图通过ArcGIS 10.8制作完成。

关于建模群落的详细描述可参阅NESP TWQ 5.4号项目的最终报告（目前处于评审阶段）。

4. 空间范围限定
海草群落类型的建模范围限定在潜在海草生境内。潜在海草生境由Carter et al. 2020建模完成，可在eAtlas平台查阅：https://eatlas.org.au/data/uuid/108ee868-4fb1-4e5f-ae57-5d65198384cc。本模型未覆盖大堡礁世界遗产区域南北外侧区域。模型覆盖大陆架区域，但排除了大陆架东侧水深约100米以上且未开展海草调查的水域。当样点延伸至大堡礁世界遗产区域边界西侧的邻近海岸与河口水域时，该类数据将被纳入分析。

5. 数据集来源
本模型使用的样点数据可通过以下链接获取：https://eatlas.org.au/data/uuid/5011393e-0db7-46ce-a8ee-f331fcf83a88
更多信息可参阅NESP TWQ 5.4号项目最终报告的待刊出版物。

6. 数据局限性
本模型使用的样点数据最早可追溯至20世纪80年代中期，自此之后，澳大利亚大片海岸区域未再开展海草分布测绘。本数据集的海草群落栅格分辨率为30米，但部分环境变量（如来自eReefs的风速、流速、底栖光照与水温数据）的空间分辨率为1公里，且这些变量在更小空间尺度上的变化未被纳入本模型。

7. 数据格式
本数据集包含6套栅格数据集，采用WGS84地理坐标系。栅格数据以附带符号系统的图层包（layer packages）形式存储，用于表征海草群落类型，具体包括：
- 河口潮间带群落：GBR_seagrass_communities_estuary_intertidal.lpk
- 河口潮下带群落：GBR_seagrass_communities_estuary_subtidal.lpk
- 海岸潮间带群落：GBR_seagrass_communities_coastal_intertidal.lpk
- 海岸潮下带群落：GBR_seagrass_communities_coastal_subtidal.lpk
- 礁体潮间带群落：GBR_seagrass_communities_reef_intertidal.lpk
- 礁体潮下带群落：GBR_seagrass_communities_reef_subtidal.lpk

8. 参考文献：待补充（TBC）

9. 数据存储位置
本数据集存储于eAtlas永久数据仓库中，路径为：data\custodian\2019-2022-NESP-TWQ-5\5.4_Seagrass-Burdekin-region

10. 许可与免责声明
TropWATER不对本数据的准确性、可靠性、完整性、时效性或适用性做出任何担保，且不对因使用本数据导致的任何损失、损害或成本（包括间接损害）承担任何责任（包括但不限于过失责任）。TropWATER保留随时更新、修改或更正本数据的权利。需充分认知并重视本数据集所包含的部分旧数据的局限性。TropWATER海草研究组期望能够审阅基于本数据开展的研究、管理、立法或合规性咨询相关文件。