Research Project Description
Our primary objective is to evaluate indicators of molecular, cellular, population, community and ecosystem responses to multiple, potentially interacting, natural and anthropogenic stressors that vary at different spatial and temporal scales in agricultural wetlands. The indicators are chosen to represent a selection of mechanism-based and system-level integrative characteristics that might be more amenable to cost-effective routine monitoring. Our null hypothesis is that indicators which effectively characterize ecosystem responses to single stressors are also scale- and interaction-independent (i.e., useful even when there are multiple, interacting stressors with diverse operational scales). Our alternative hypothesis is that when multiple, interacting stressors are present, responses are not well characterized by indicators that are useful for monitoring the effects of single stressors. This outcome demands the use of either a different set of indicators or a different spatial or temporal scale of resolution for evaluating the indicators.
The indicators will be identified first in a set of 72 wetland mesocosms where individual and multiple, interacting, stressors are used as treatments in a controlled outdoor experiment. We will manipulate a suite of anthropogenic and natural stressors--the concentrations of chlorpyrifos and MSMA, UV radiation and wetland depth. Post-experiment analysis will focus on the efficacy of each indicator to discriminate among different stressors or combinations of stressors. Successful indicators will then be evaluated in agricultural wetlands identified by analysis of northern Mississippi agricultural landscapes, based on remote imagery and other landscape data, to have the same stressors or combinations of stressors present that were included in the mesocosm experiment. Scale dependency of the stressor-indicator relationship will be evaluated.
The principal benefit of our proposed research, beyond an increased understanding of ecological processes operating in agricultural ecosystems, is that we will: 1. identify and evaluate scale- and interaction-independent indicators; 2. provide data that can be used to select and incorporate indicators into a monitoring program that will be independent of spatial scale; and 3. provide data that can be used in a predictive manner to assist in policy and management decisions regarding agrichemical use.
Last Modified: Thursday, 3 September 1998
Copyright © 1998 Stephen Threlkeld, University of Mississippi. All Rights reserved.