Relationships between algal primary productivity and environmental variables in tropical floodplain wetlands

Abstract

Floodplain wetlands are among the most productive and biodiverse ecosystems on Earth and provide a major subsidy of food resources for consumers in river systems. The basal energy source for those consumers in many systems comes from aquatic algal production influenced by different characteristics of the floodplain environment. Our aim was to estimate relationships between algal productivity and environmental variables in the channels and wetlands of the Mitchell River floodplain in tropical Australia. We measured physical, chemical, and biological variables in a range of different wetland types (palustrine, lacustrine, and riverine) and different habitat types (emergent macrophytes, floating macrophytes, submerged macrophytes, and open water). The most productive areas were found among riverine wetlands and submerged habitats. The statistical models showed that habitat type and turbidity alone can predict algal productivity with reasonable accuracy (pseudo-R 2 = 0.35, n = 63). Importantly, those attributes can be measured using remote sensing, and hence the model can be used to predict algal productivity over wider spatial scales and identify important "hotspot" areas of primary productivity that sustain aquatic food webs. Through this approach we can inform current conservation and water planning frameworks to manage the impact of human development on the productivity of wetlands at large spatial scales.

Assessing Spatial Variation in Algal Productivity in a Tropical River Floodplain Using Satellite Remote Sensing

“…Rates of algal production across the floodplain were obtained via an additional statistical model that used predicted spatial variation in turbidity and habitat as inputs. In-situ data were obtained in a single sampling campaign conducted in April 2018 [9] and were used to calibrate and perform the accuracy assessments of the statistical models of turbidity and algal productivity. Additionally, predictive intervals were calculated for algal productivity predictions.…”

“…In 2018, the wet season was delayed and most of the rainfall and discharge at Dunbar gauging station happened between February and April (Figure A1). The Mitchell River floodplain supports a large number of off-stream wetlands [28], which in turn support aquatic macrophytes and algal production [9,25] thereby playing an important role in sustaining aquatic biodiversity. The catchments of tropical rivers in northern Australia, which includes the Mitchell River, have low levels of agricultural, urban, or water resource development compared with other regions of the country [29].…”

“…Aquatic food webs on river floodplains depend on primary producers such as phytoplankton and epiphyton as the major basal food sources that support higher order consumers [1][2][3][4]. Because algae are such an important food resource for higher trophic levels in river systems [5], numerous studies across different ecosystems have developed methods to measure rates of production [6][7][8][9][10][11]. In general, estimating rates of algal production provides more information than algal biomass about ecosystem function, because observed biomass is the outcome of several processes [12].…”

“…Such field-based measurements provide crucial information at local scales that can also be integrated into statistical models and combined with remote sensing data to predict algal productivity in new locations [9]. This can be particularly valuable in remote areas and for large floodplain systems where, field work can be constrained by logistical challenges limiting access during periods of inundation, but for which data from remote sensing methods may be available.…”

“…This study builds upon a previous local, field-data-based statistical model developed to predict the spatial variation of algal productivity in the Mitchell River, northern Australia [9], aiming to expand its application to the floodplain scale by including satellite remotely sensed data. This previous research showed that turbidity and macrophyte cover, or its absence, could be used to predict rates of phytoplankton and epiphyte production with reasonable accuracy [9] and both of these predictor variables can be observed via remote sensing [20,[23][24][25][26]. In this study, we use Landsat 8 images to remotely predict turbidity and macrophyte cover in order to facilitate algal productivity predictions across the Mitchell River floodplain.…”