Guillaume Annie S, Ferrari Renata, Selmoni Oliver, Mocellin Véronique J L, Denis Hugo, Naugle Melissa, Howells Emily, Bay Line K, Joost Stéphane
Geospatial Molecular Epidemiology Group (GEOME), Laboratory for Biological Geochemistry (LGB) École Polytechnique Fédérale de Lausanne (EPFL) Lausanne Switzerland.
Australian Institute of Marine Science Townsville Queensland Australia.
Ecol Evol. 2025 Apr 23;15(4):e71292. doi: 10.1002/ece3.71292. eCollection 2025 Apr.
Effective biodiversity conservation requires knowledge of species' distributions across large areas, yet prevalence data for marine sessile species is scarce, with traditional variables often unavailable at appropriate temporal and spatial resolutions. As marine organism distributions generally depend on terrain heterogeneity, topographic variables derived from digital elevation models (DEMs) can be useful proxies in ecological modelling, given appropriate spatial resolutions. Here, we use three reef-building coral species across the Great Barrier Reef, Australia, in a case study to (1) assess high-resolution bathymetry DEM sources for accuracy, (2) harness their derived topographic variables for regional coral species distribution models (SDMs), and (3) develop a transferable framework to produce, select and integrate multi-resolution variables into marine spatial models. For this, we obtained and processed three distinct bathymetric digital depth models that we treat as DEMs, which are available across the GBR extent: (i) Allen Coral Atlas (ACA) at 10 m, (ii) DeepReef at 30 m and (iii) DeepReef at 100 m. We generalised the three DEMs to multiple nested spatial resolutions (15 m-120 m) and derived the same eight topographic variables to assess SDM sensitivity to bathymetry source and spatial resolution. The ACA and DeepReef DEMs shared similar vertical accuracies, each producing topographic variables relevant to marine SDMs. Slope and vector ruggedness measure (VRM), capturing hydrodynamic movement and shelter or exposure, were the most relevant variables in SDMs of all three species. Interestingly, variables at the finest resolution (15 m) were not always the most relevant for producing accurate coral SDMs, with optimal resolutions between 15 and 60 m depending on the variable type and species. Using multi-resolution topographic variables in SDMs provided nuanced insights into the multiscale drivers of regional coral distributions. Drawing from this case study, we provide a practical and transferable framework to facilitate the adoption of multiscale SDMs for better-informed conservation and management planning.
有效的生物多样性保护需要了解物种在大片区域的分布情况，然而，海洋固着生物物种的流行数据却很稀少，传统变量往往无法以适当的时间和空间分辨率获取。由于海洋生物的分布通常取决于地形的异质性，在具有适当空间分辨率的情况下，从数字高程模型（DEM）中得出的地形变量在生态建模中可能是有用的替代指标。在此，我们以澳大利亚大堡礁的三种造礁珊瑚物种为例进行研究，以（1）评估高分辨率测深DEM来源的准确性，（2）利用其导出的地形变量构建区域珊瑚物种分布模型（SDM），以及（3）开发一个可转移的框架，以生成、选择多分辨率变量并将其整合到海洋空间模型中。为此，我们获取并处理了三种不同的测深数字深度模型，我们将其视为DEM，这些模型在大堡礁范围内均可获取：（i）10米分辨率的艾伦珊瑚地图集（ACA），（ii）30米分辨率的DeepReef，以及（iii）100米分辨率的DeepReef。我们将这三种DEM概括为多个嵌套的空间分辨率（15米 - 120米），并导出相同的八个地形变量，以评估SDM对测深来源和空间分辨率的敏感性。ACA和DeepReef DEM具有相似的垂直精度，各自生成与海洋SDM相关的地形变量。坡度和矢量粗糙度测量（VRM），用于捕捉水动力运动以及遮蔽或暴露情况，是所有三种物种的SDM中最相关的变量。有趣的是，最精细分辨率（15米）的变量并不总是对生成准确的珊瑚SDM最相关，根据变量类型和物种的不同，最佳分辨率在15米至60米之间。在SDM中使用多分辨率地形变量为区域珊瑚分布的多尺度驱动因素提供了细致入微的见解。基于此案例研究，我们提供了一个实用且可转移的框架，以促进采用多尺度SDM进行更明智的保护和管理规划。
