Increased sediment loads cause non-linear decreases in seagrass suitable habitat extent.
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ABSTRACT: Land-based activities, including deforestation, agriculture, and urbanisation, cause increased erosion, reduced inland and coastal water quality, and subsequent loss or degradation of downstream coastal marine ecosystems. Quantitative approaches to link sediment loads from catchments to metrics of downstream marine ecosystem state are required to calculate the cost effectiveness of taking conservation actions on land to benefits accrued in the ocean. Here we quantify the relationship between sediment loads derived from landscapes to habitat suitability of seagrass meadows in Moreton Bay, Queensland, Australia. We use the following approach: (1) a catchment hydrological model generates sediment loads; (2) a statistical model links sediment loads to water clarity at monthly time-steps; (3) a species distribution model (SDM) factors in water clarity, bathymetry, wave height, and substrate suitability to predict seagrass habitat suitability at monthly time-steps; and (4) a statistical model quantifies the effect of sediment loads on area of seagrass suitable habitat in a given year. The relationship between sediment loads and seagrass suitable habitat is non-linear: large increases in sediment have a disproportionately large negative impact on availability of seagrass suitable habitat. Varying the temporal scale of analysis (monthly vs. yearly), or varying the threshold value used to delineate predicted seagrass presence vs. absence, both affect the magnitude, but not the overall shape, of the relationship between sediment loads and seagrass suitable habitat area. Quantifying the link between sediment produced from catchments and extent of downstream marine ecosystems allows assessment of the relative costs and benefits of taking conservation actions on land or in the ocean, respectively, to marine ecosystems.
SUBMITTER: Saunders MI
PROVIDER: S-EPMC5681285 | biostudies-literature | 2017
REPOSITORIES: biostudies-literature
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