Provoking a Cultural Shift in Data Quality.

Ecological studies require quality data to describe the nature of ecological processes and to advance understanding of ecosystem change. Increasing access to big data has magnified both the burden and the complexity of ensuring quality data. The costs of errors in ecology include low use of data, increased time spent cleaning data, and poor reproducibility that can result in a misunderstanding of ecosystem processes and dynamics, all of which can erode the efficacy of and trust in ecological research. Although conceptual and technological advances have improved ecological data access and management, a cultural shift is needed to embed data quality as a cultural practice. We present a comprehensive data quality framework to evoke this cultural shift. The data quality framework flexibly supports different collaboration models, supports all types of ecological data, and can be used to describe data quality within both short- and long-term ecological studies.

Managing flood flow connectivity to landscapes to build buffering capacity to disturbances: An ecohydrologic modeling framework for drylands.

Increased flooding, droughts, and sediment transport are watershed-scale problems negatively impacting agriculture and ecosystems in drylands worldwide. Vegetation loss in upland watersheds is leading to scouring floods, which in turn decreases infiltration, soil moisture levels, and downstream groundwater recharge. Management to confront these intractable problems has been hindered by a lack of accessible decision support tools for both land and water managers that synthesize the watershed processes that buffer against dryland disturbances. Flood flow connectivities across the landscape create buffer zones through replenishing soil moisture and reducing flood energy, which in turn support multiple functions. This study developed a decision support tool, the Flood Flow Connectivity to the Landscape (FlowCon) framework that quantifies the most efficient management efforts to increase the key watershed buffering functions of increasing infiltration and reducing flow energy. FlowCon links three spatially explicit, process-based, and predictive models to answer two critical management questions: what key processes acting in what optimal areas are drivers of infiltration dynamics and what roles do peak flows of differing scales of energy play. The spatial models delineated the buffer zone to characterize the heterogeneous and optimal infiltration dynamics across the landscape. The hydrologic process model, using a curve number technique, identified the key ecohydrologic processes that affect infiltration and characterized peak flows and flow regime variability. The predictive flood routing model quantified the potential management benefits. We calibrated the models with measured runoff and the corresponding rainfall events for a six-year period, which included thirty-six flow events. The synthesized ecohydrologic indicators provided critical calibrations, improving the relationship between the hydrologic modeling results and observed data by 12% for the linear regression R2 and 69% for the root mean square error (RMSE). Implementation of prioritized management is estimated to reduce peak flow by half, with interventions focused on 24% of floodplains that infiltrate three times the flow volume per area than the floodplain average. FlowCon provides an efficient assessment framework that integrates watershed process understanding in an accessible decision support tool to achieve tangible improvements in dryland watershed management.

Quadrat-based monitoring of desert grassland vegetation at the Jornada Experimental Range, New Mexico, 1915-2016.

The data set covers a 101-yr period (1915-2016) of quadrat-based plant sampling at the Jornada Experimental Range in southern New Mexico. At each sampling event, a pantograph was used to record the location and perimeter of living plants within permanent quadrats. Basal area was recorded for perennial grass species, canopy cover area was recorded for shrub species, and all other perennial species were recorded as point data. The data set includes 122 1 × 1 m permanent quadrats, although not all quadrats were sampled in each year of the study and there is a gap in monitoring from 1980 to 1995. These data provide a unique opportunity to investigate changes in the plant community over 100 yr of variation in precipitation and other environmental conditions. We provide the following data and data formats: (1) the digitized maps in shapefile format; (2) a data table containing coordinates (x, y) of perennial species within quadrats, including cover area for grasses and shrubs; (3) a data table of counts of annual plant individuals per quadrat; (4) a species list indicating growth form and habit of recorded species; (5) a table of dates when each quadrat was sampled; (6) a table of the pasture each quadrat was located within (note that pasture boundaries have changed over time); (7) a table of depth to petrocalcic layer measurements taken at quadrat locations; (8) a table of particle size analysis of soil samples taken at quadrat locations; (9) a table of topographic characteristics of quadrat locations (e.g., concave or convex topography). Pantograph sampling is currently conducted at 5-yr intervals by USDA-ARS staff, and new data will be added periodically to the EDI Data Portal Repository (see section V.E.2). This information is released under the Creative Commons license-Attribution-CC BY and the consumer of these data is required to cite it appropriately in any publication that results from its use.