Mapping the dynamics of aquatic vegetation in Lake Kyoga and its linkages to satellite lakes

Abstract

Lake Kyoga is a shallow, young, flooded basin just north of and about 30m lower than Lake Victoria. The catchment encompasses Lake Kyoga itself, and a constellation of several dozen small satellite lakes following valley contours mostly to its east. The Kyoga basin fish fauna shares many non-cichlid species plus a spectacular, partially endemic radiation of haplochromine cichlids most similar to but still largely distinct from those in Lake Victoria. This fish fauna is of high conservation concern, as it preserves remnants of the regional species flock that have disappeared from Lake Victoria and Lake Kyoga, leaving small remnant populations in some of the satellite lakes. Now, these too are imperiled by limnological dynamics, including fluctuations in the nature and extent of aquatic vegetation. The water bodies in the Kyoga Basin are highly dynamic due both to fluctuation in water level and large amplitude variation in marginal and floating vegetation. This variation has profound evolutionary and conservation implications, since it can create and destroy critical aquatic habitat. It can also alternately anneal and cleave gene flow over time, both between the main lake and its satellites, and among the satellite lakes. The aquatic vegetation cluttering these linkages can create a spatial refugium for many native fish species that are more tolerant of hypoxia than an introduced macropredator, the Nile perch. Anthropogenic impacts to this region have greatly increased in recent years, altering relationships between aquatic vegetation and endangered species, fisheries and other ecosystem services provided by the lake. Understanding these dynamics require a means of mapping aquatic vegetation, connectivity, and habitat through time. Here we develop a new and improved algorithm to map the spatial distribution and dynamics of floating and emergent aquatic vegetation via remote sensing. We utilize a time series of 440 Landsat images dating from 1986 to 2020. A series of water and vegetation indices are designed to reveal change in the aquascape over time. First, two types of water masks are derived using a majority rule - a separate water mask for each image and a composite water mask of the region over the study period. Second, the difference between the two masks is then used to delineate the potential location of macrophytes over the image. Third, an algorithm is developed to separate the floating vegetation from emergent vegetation; this algorithm uses Landsat spectral bands and two additional spatial and temporal metrics that considerably improve classification accuracy. A more extensive analysis of aquascape trajectories using remote sensing can inform fish conservation strategies and fisheries management and illuminate the role of landscape dynamics in macroevolutionary patterns of aquatic taxa.