Qualifying the effects of single and multiple stressors on the food web structure of Dutch drainage ditches using a literature review and conceptual models

In September 2017, a workshop was held at Wageningen University and Research to determine the current state of knowledge of multiple stressor effects on aquatic ecosystems and to assess how to improve prediction of these effects. We developed a theoretical framework that integrates species-level responses to stressors to predict how these effects propagate through higher levels of biological organisation. Here, we present the application of the framework for drainage ditch ecosystems in the Netherlands. We used food webs to assess single and multiple stressor effects of common stressors on ditch communities. We reviewed the literature for the effects of targeted stressors (nutrients, pesticides, dredging and mowing, salinization, and siltation) on each functional group present in the food web and qualitatively assessed the relative sensitivity of groups. Using this information, we created a stressor-response matrix of positive and negative direct effects of each stressor-functional group combination. Fungicides, salinization, and sedimentation were identified as particularly detrimental to most groups, although destructive management practices, such as dredging with almost complete community removal, would take precedence depending on frequency. Using the stressor-response matrix we built, first, a series of conceptual null models of single stressor effects on food web structure and, second, a series of additive null models to illustrate potential paired-stressor effects. We compared these additive null models with published studies of the same pairs of combined single stressors to explore more complex interactions. Our approach serves as a first-step to considering multiple stressor scenarios in systems that are understudied or data-poor and as a baseline from which more complex models that include indirect effects and quantitative data may be developed. We make specific suggestions for appropriate management strategies that could be taken to support the biodiversity of these systems for individual stressors and their combined impacts

Using vegetation indices from satellite remote sensing to assess corn and soybean response to controlled tile drainage

Controlled tile drainage (CTD) is a management practice designed to retain water and nutrients in the field for crop use. CTD has shown promise for improving water quality and augmenting crop yields but findings are often restricted to field and plot scales. Remote sensing is one of the alternatives to evaluate crop responsiveness to CTD at large spatial scales. This study compared normalized and green normalized difference vegetation indices (NDVI and GNDVI) for corn (Zea mays L.) and soybean (Glycine max L.) among CTD and uncontrolled tile drainage (UCTD) fields in a ~950 ha experimental watershed setting in Ontario, Canada from 2005 to 2008. The indices were derived from Landsat-5 and SPOT-4 satellite imagery. Log-transformed NDVI and GNDVI for soybean (R3-R6 growth stage) and corn (VT to R5-R6 growth stage) crops were higher significantly (p   CTD). Log-transformed NDVI and GNDVI standard errors for CTD, relative to UCTD fields, were lower for 65% of the significant corn and 71% of the significant soybean NDVI and GNDVI comparisons for the growth stages noted above. This finding suggested overall more uniform crop growth for CTD fields relative to UCTD fields. Observed yields from a subset of commonly managed CTD and UCTD fields in the study area were not significantly different from each other (p > 0.05) with respect to tile drainage management practice; however, 87% of these paired yield comparisons indicated that CTD mean corn/soybean grain yields were greater than or equal to those for UCTD. On average, CTD observed corn and soybean grain yields were 3% and 4%, respectively, greater than those from UCTD. From observed yield and NDVI and GNDVI observations, vegetation indices vs. yield linear regression models were developed to predict grain yields over a broader land base in the experimental watershed area. Here, predicted mean yields were 0.1-11% higher for CTD corn and -5% to 4% higher for CTD soybean, relative to UCTD crops; but results varied between manured and non-manured fertilizer practices. Eighty-nine percent of the standard deviations for these yield predictions were lower for CTD relative to UCTD. The results of this study indicate that at a minimum, CTD did not adversely impact corn and soybean grain yields over the time span and field environments of the study, and based on the weight of evidence presented here, CTD shows general promise for augmenting crop performance. Finally, remote sensing derived vegetation indices such as NDVI and GNDVI can be used to assess the impact of agricultural drainage management practices on crop response and production properties.Controlled tile drainage (CTD) Uncontrolled tile drainage (UCTD) Normalized Difference Vegetation Index (NDVI) Green Normalized Difference Vegetation Index (GNDVI) Remote sensing Crop monitoring Grain yield