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Global hydro-environmental lake characteristics at high spatial resolution

ABSTRACT: Here we introduce the LakeATLAS dataset, which provides a broad range of hydro-environmental characteristics for more than 1.4 million lakes and reservoirs globally with an area of at least 10 ha. LakeATLAS forms part of the larger HydroATLAS data repository and expands the existing datasets of sub-basin and river reach descriptors by adding equivalent information for lakes and reservoirs in a compatible structure. Matching its HydroATLAS counterparts, version 1.0 of LakeATLAS contains data for 56 variables, partitioned into 281 individual attributes and organized in six categories: hydrology; physiography; climate; land cover & use; soils & geology; and anthropogenic influences. LakeATLAS derives these attributes by processing and reformatting original data from well-established global digital maps at 15 arc-second (~500 m) grid cell resolution and assigns the information spatially to each lake by aggregating it within the lake, in a 3-km vicinity buffer around the lake, and/or within the entire upstream drainage area of the lake. The standardized format of LakeATLAS ensures versatile applicability in hydro-ecological assessments from regional to global scales. Measurement(s)hydro-environmental characteristics • lake • water body • hydrographic featureTechnology Type(s)digital curationSample Characteristic - Environmentfreshwater environment • aquatic environmentSample Characteristic - LocationEarth (planet)

SUBMITTER: Lehner B

PROVIDER: S-EPMC9226168 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundGlobal regression models under an implicit assumption of spatial stationarity were commonly applied to estimate the environmental effects on aquatic species distribution. However, the relationships between species distribution and environmental variables may change among spatial locations, especially at large spatial scales with complicated habitat. Local regression models are appropriate supplementary tools to explore species-environment relationships at finer scales.MethodWe applied geographically weighted regression (GWR) models on Yellow Perch in Lake Erie to estimate spatially-varying environmental effects on the presence probabilities of this species. Outputs from GWR were compared with those from generalized additive models (GAMs) in exploring the Yellow Perch distribution. Local regression coefficients from the GWR were mapped to visualize spatially-varying species-environment relationships. K-means cluster analyses based on the t-values of GWR local regression coefficients were used to characterize the distinct zones of ecological relationships.ResultsGeographically weighted regression resulted in a significant improvement over the GAM in goodness-of-fit and accuracy of model prediction. Results from the GWR revealed the magnitude and direction of environmental effects on Yellow Perch distribution changed among spatial locations. Consistent species-environment relationships were found in the west and east basins for adults. The different kinds of species-environment relationships found in the central management unit (MU) implied the variation of relationships at a scale finer than the MU.ConclusionsThis study draws attention to the importance of accounting for spatial nonstationarity in exploring species-environment relationships. The GWR results can provide support for identification of unique stocks and potential refinement of the current jurisdictional MU structure toward more ecologically relevant MUs for the sustainable management of Yellow Perch in Lake Erie.

Project description:The coexistence of species with similar ecological niches is one of the core interests of community ecology research. However, how functional feeding traits, including bill size and leg length, determine the niche of mixed flocks of shorebird species has seldomly been studied, as well as, microhabitat variables affect the spatial patterns of availability and the quality of patches for wintering. From October 2016 to March 2017 at Shengjin Lake, Anhui Province, China, we recorded 226 scan samples from the different microhabitats and 93 focal animal videos of four common shorebird species: common greenshank, spotted redshank, Kentish plover, and little ringed plover. We found that the species participating in the mixed groups were different in each microhabitat. The results of the overlap index for microhabitats and foraging techniques between the species were consistent with the morphological characteristics of these species. Kentish and little ringed plovers had the highest Pianka's niche overlap index values of 0.95 and 0.98 for microhabitats and foraging techniques, respectively, whereas common greenshank and spotted redshank had values of 0.78 and 0.89, respectively. Common greenshank and spotted redshank used four foraging techniques: a single probe (PR), multiple probes (MPR), a single peck (PE), and multiple pecks (MPE). Kentish and little ringed plovers only used PE and MPE. The mean bill size, mean leg length, and mean foraging frequency were significantly associated with water depth. The mean bill size and mean leg length were both significantly correlated with the mean foraging frequency of shorebirds. The vegetated area was the most important variable for grouping among shorebirds. We concluded that the four species showed differences in their preferred microhabitats and foraging patterns. Interspecific morphological differences, including bill and leg lengths, resulted in niche differentiation. Thus, effective resource allocation by regional species was realized, and a dynamic balance was achieved by the mixed foraging species. The information on foraging behavior and habitat requirements could be useful in the management of water levels in natural areas and conservation of a diversity of wintering shorebirds.

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Global hydro-environmental lake characteristics at high spatial resolution

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