Analysis of macrophyte indicator variation as a function of sampling, temporal, and stressor effects
Introduction
Aquatic macrophytes have become increasingly relevant for their use as ecological indicators since numerous research efforts have shown that plant community response to environmental stressors can be predictable and diagnostic of ecosystem change. The usefulness of macrophyte-based indicators for lake monitoring can be attributed to several characteristics that distinguish macrophytes from other taxa. Macrophytes are sedentary, easily sampled and identified, and serve as integrative indicators of environmental condition through the accumulation of biomass over time and links with other trophic levels (Jeppesen et al., 1998, Rooney and Kalff, 2000). Macrophyte indicators have been used in the United States to address biological integrity requirements under the Clean Water Act (Rothrock et al., 2008, Beck et al., 2010, Radomski and Perleberg, 2012) and in Europe to address biological quality elements of the Water Framework Directive (Penning et al., 2008). Although the widespread adoption of macrophyte-based indicators across broad geographic scales has improved efforts for evaluating ecosystem change, the need to characterize indicator variation patterns and relationships to environmental characteristics remains a relevant research topic. The identification of macrophyte indicators that are responsive to a range of environmental stressors with acceptable precision is the focus of this paper.
An additional challenge for the development of biological indicators is the selection of community characteristics that are diagnostic of relevant stressors present within an ecosystem. Indicators can be developed that measure various aspects of macrophyte communities, such as diversity or richness-based measures, composite or multimetric indices, structural-based indicators, or the identification of individual species that are diagnostic of specific conditions. The selection of an appropriate indicator is primarily defined by the management or research need, although a general assumption has been that no single indicator is appropriate to address all needs (Cairns et al., 1993). Multimetric indices, such as an index of biotic integrity, have been developed in part to address the need to integrate several indicators into a single index (Beck et al., 2010). Indices based on multiple indicators can provide useful information about ecosystem change from a wide range of ecosystem stressors; however, recent analyses for macrophyte communities in Minnesota lakes suggest index scores may provide comparable information about community structure relationships to environmental stressor gradients as more simplistic measures (Radomski and Perleberg, 2012). Dependent on monitoring goals, a simpler measure may be appropriate to characterize community status, while the use of several indicators may provide the most practical approach for detecting effects of a wide range of potential stressors.
A desirable characteristic of a biological indicator is a high signal-to-noise ratio, such that the indicator is responsive to environmental degradation while exhibiting minimal variation related to sampling or natural variability over time. Sampling variation is described by the distribution of replicate measurements while the system is under identical conditions (Dolph et al., 2010). Natural variation in indicator values, often called process variance, is attributed to annual variation in species occurrences and abundances in an unchanged ecosystem. An evaluation of indicator variation across multiple years with repeated surveys is a necessary approach for characterizing both sampling and natural noise and is often over-looked during indicator selection (Dolph et al., 2010). Estimates of sampling variance are necessary to ensure adequate sampling effort for surveys to achieve desired levels of precision in estimates of current indicator values, while process variance will affect the ability to see changes in average indicator values over time due to ecosystem changes. The ability to detect such a response of indicators to environmental stressors, i.e., the “signal”, is an important issue that requires evaluation relative to established research or management objectives. Macrophyte indicators have typically been evaluated based on their response to watershed (e.g., Søndergaard et al., 2010) or shoreline development (Radomski and Goemann, 2001, Sass et al., 2010). Most published studies have been comparative, evaluating differences between groups of lakes that express a range of stressor conditions. Rarely has natural, within-lake annual variability of indicators been included in predictions of the degree of environmental change that would be needed to detect significant change in indicator values (i.e., power). Tracking sensitive indicators with low natural variability is paramount given the shifting baseline of climate change and its potential to exacerbate the outcomes of conventional stressors (e.g., nutrient loading due to runoff). A climate “signal” must be detected through random “noise” (due to both sampling and natural variability) for effective management responses.
The goals of this study were to quantify the variation (sampling and natural), response, and power of eight macrophyte indicators in 23 diverse Minnesota lakes sampled repeatedly over four years. Indicators included a macrophyte index of biotic integrity, floristic quality index, maximum depth of growth, total species richness, common species richness, mean richness at survey points, and frequency occurrence of rooted species and Chara sp. The three main objectives of the analysis were to (1) quantify sampling and natural temporal variation of indicators within and among lakes, (2) identify patterns in indicator variation related to total phosphorus (a measure of development stress) and mean annual growing degrees days (a measure of climate warming), and (3) quantify the minimum sampling effort (in terms of frequency of repeated surveys) necessary for acceptable power to detect a considerable change in indicator values over a finite period of observation. Fundamental to our approach was the recognition that variation among indicators may be related to a combination of sampling, temporal, or stressor effects. We considered an indicator to be potentially useful based on its relationship to stressor levels among lakes while exhibiting minimal sampling and temporal variation within lakes; however, natural indicator variation itself may differ in response to disturbance status (i.e., indicators in a highly disturbed system may exhibit higher natural variation than in an undisturbed one). In this case, a desirable indicator would have considerable within-lake variation in a disturbed system, while exhibiting lower natural variation in an undisturbed lake.
Section snippets
Study area and data collection
Macrophyte surveys were conducted in four consecutive years from 2008 to 2011 on 23 Minnesota lakes as part of the Sustaining Lakes in a Changing Environment (SLICE) program managed by the Minnesota Department of Natural Resources (MNDNR) (2013), (Fig. 1). Lakes were chosen to represent the diversity of lake types in Minnesota using a stratified sampling design. Specifically, lakes were chosen to provide equal representations from the four major land types in the state (Canadian Shield, Glacial
Indicator patterns and variation
Substantial variation of indicators was observed among the 23 lakes (Table 3, Fig. 2). The macrophyte IBI ranged from 19 (Artichoke) to 79 (Cedar), which exceeds the range for a set of Minnesota lakes evaluated by Beck et al. (2010). FQI varied from 5 (Carrie) to 39 (Bear Head), MaxDepth from 0.6 (Carrie) to 5.5 m (Carlos), RichTot from 1 (Trout) to 43 spp. (Bear Head), RichCom from 0 (several lakes) to 25 spp. (Hill), MeanPt from 0.02 (Artichoke) to 3.97 spp. per point (Hill), Rooted from 1.6
Factors influencing indicator variation
Indicator evaluation within lakes suggested that natural variability among years likely explained differing rates of power among the indicators. In particular, our results showed that both IBI and FQI are robust indicators that exhibit both minimal sampling variation at the levels of sampling intensity in this study and the ability to detect significant changes over time with power greater than 50% for most of the evaluated scenarios (Fig. 6). Both indicators are composed of more than one
Acknowledgements
Thanks to Pete Jacobson for providing helpful comments which improved an earlier draft of the manuscript. The authors also acknowledge the extensive efforts of area managers and field crews for obtaining the data used in our analyses.
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