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Environmental Stressors Influence Spatial Complexity in a Fluvial Macrophyte meadow
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DOI：10.1002/rse2.70084.png)
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Abstract
En 中文
Submerged aquatic vegetation (SAV) are a key element of aquatic ecosystems, having a strong influence on their structure and functioning. Because of their key effect on aquatic habitats and the scale-dependent mechanisms driving their distribution (e.g., local facilitation vs. stress driven inhibition), we expect the spatial complexity of SAV landscapes to be an indicator of ecological integrity and ecosystem resilience. In this paper, we analyze the fine-scale spatial complexity of SAV along spatial gradients of environmental stress in the largest fluvial lake of the St. Lawrence River, in Quebec, Canada. The hypothesis that spatial complexity metrics of SAV landscapes are sensitive to environmental changes was tested by using very high-resolution remote sensing and a public database of field observations related to water quality and SAV. Multivariate spatial correlation analysis was used to generate a composite index of spatial complexity, as well as to define dominant environmental gradients. In this specific context, generalized additive models revealed that, while water depth and SAV abundance are strong predictors of spatial complexity, variation explained by the interaction between depth, SAV abundance, and water quality was an important predictor for the multidimensional variation of spatial complexity. Specifically, both light and nutrient limitations correlated with a decrease in SAV spatial complexity, especially in dense meadows. Despite the limits of the space-for-time substitution approach and the use of context-dependent methods, this work represents a rare example of a remote sensing-based application of the use of SAV as a fine scale ecological indicator. These results suggest that a systematic monitoring of SAV by remote sensing over long time periods could offer high-value ecological data for both conservation and management.
Keywords:
GEOBIA
landscape ecology
macrophytes
optical remote sensing
SAV
very high resolution
Journal
IF:
4.3
Papers:
447
Citations:
1.9K
