Zooplankton-phytoplankton biomass and diversity relationships in the Great Lakes.

Quantifying the relationship between phytoplankton and zooplankton may offer insight into zooplankton sensitivity to shifting phytoplankton assemblages and the potential impacts of producer-consumer decoupling on the rest of the food web. We analyzed 18 years (2001-2018) of paired phytoplankton and zooplankton samples collected as part of the United States Environmental Protection Agency (U.S. EPA) Great Lakes Biology Monitoring Program to examine both the long-term and seasonal relationships between zooplankton and phytoplankton across all five Laurentian Great Lakes. We also analyzed effects of phytoplankton diversity on zooplankton biomass, diversity, and predator-prey (zooplanktivore/grazer) ratios. Across the Great Lakes, there was a weak positive correlation between total algal biovolume and zooplankton biomass in both spring and summer. The relationship was weaker and not consistently positive within individual lakes. These trends were consistent over time, providing no evidence of increasing decoupling over the study period. Zooplankton biomass was weakly negatively correlated with algal diversity across lakes, whereas zooplankton diversity was unaffected. These relationships did not change when we considered only the edible phytoplankton fraction, possibly due to the high correlation between total and edible phytoplankton biovolume in most of these lakes. Lack of strong coupling between these producer and consumer assemblages may be related to lagging responses by the consumers, top-down effects from higher-level consumers, or other confounding factors. These results underscore the difficulty in predicting higher trophic level responses, including zooplankton, from changes in phytoplankton assemblages.

Utility of a PCR-based method for rapid and specific detection of toxigenic Microcystis spp. in farm ponds.

Microcystis is a widespread freshwater cyanobacterium that can produce microcystin, a potent hepatotoxin harmful to animals and humans. Therefore, it is crucial to monitor for the presence of toxigenic Microcystis spp. to provide early warning of potential microcystin contamination. Microscopy, which has been used traditionally to identify Microcystis spp., cannot differentiate toxigenic from non-toxigenic Microcystis. We developed a PCR-based method to detect toxigenic Microcystis spp. based on detection of the microcystin synthetase C (mcyC) gene and 16S rRNA gene. Specificity was validated against toxic and nontoxic M. aeruginosa strains, as well as 4 intergeneric freshwater cyanobacterial strains. Analytical sensitivity was as low as 747 fg/µL genomic DNA (or 3 cells/µL) for toxic M. aeruginosa. Furthermore, we tested 60 water samples from 4 farm ponds providing drinking water to swine facilities in the midwestern United States using this method. Although all water samples were positive for Microcystis spp. (i.e., 16S rRNA gene), toxigenic Microcystis spp. were detected in only 34 samples (57%). Seventeen water samples contained microcystin (0.1-9.1 µg/L) determined with liquid chromatography-mass spectrometry, of which 14 samples (82%) were positive for mcyC. A significant correlation was found between the presence of toxigenic Microcystis spp. and microcystin in water samples (p = 0.0004). Our PCR method can be a low-cost molecular tool for rapid and specific identification of toxigenic Microcystis spp. in farm ponds, improving detection of microcystin contamination, and ensuring water safety for farm animals.

Ecological prediction at macroscales using big data: Does sampling design matter?

Although ecosystems respond to global change at regional to continental scales (i.e., macroscales), model predictions of ecosystem responses often rely on data from targeted monitoring of a small proportion of sampled ecosystems within a particular geographic area. In this study, we examined how the sampling strategy used to collect data for such models influences predictive performance. We subsampled a large and spatially extensive data set to investigate how macroscale sampling strategy affects prediction of ecosystem characteristics in 6,784 lakes across a 1.8-million-km2 area. We estimated model predictive performance for different subsets of the data set to mimic three common sampling strategies for collecting observations of ecosystem characteristics: random sampling design, stratified random sampling design, and targeted sampling. We found that sampling strategy influenced model predictive performance such that (1) stratified random sampling designs did not improve predictive performance compared to simple random sampling designs and (2) although one of the scenarios that mimicked targeted (non-random) sampling had the poorest performing predictive models, the other targeted sampling scenarios resulted in models with similar predictive performance to that of the random sampling scenarios. Our results suggest that although potential biases in data sets from some forms of targeted sampling may limit predictive performance, compiling existing spatially extensive data sets can result in models with good predictive performance that may inform a wide range of science questions and policy goals related to global change.

Evenness effects mask richness effects on ecosystem functioning at macro-scales in lakes.

Biodiversity-ecosystem functioning (BEF) theory has largely focused on species richness, although studies have demonstrated that evenness may have stronger effects. While theory and numerous small-scale studies support positive BEF relationships, regional studies have documented negative effects of evenness on ecosystem functioning. We analysed a lake dataset spanning the continental US to evaluate whether strong evenness effects are common at broad spatial scales and if BEF relationships are similar across diverse regions and trophic levels. At the continental scale, phytoplankton evenness explained more variance in phytoplankton and zooplankton resource use efficiency (RUE; ratio of biomass to resources) than richness. For individual regions, slopes of phytoplankton evenness-RUE relationships were consistently negative and positive for phytoplankton and zooplankton RUE, respectively, and most slopes did not significantly differ among regions. Findings suggest that negative evenness effects may be more common than previously documented and are not exceptions restricted to highly disturbed systems.

LAGOS-NE: a multi-scaled geospatial and temporal database of lake ecological context and water quality for thousands of US lakes.

Understanding the factors that affect water quality and the ecological services provided by freshwater ecosystems is an urgent global environmental issue. Predicting how water quality will respond to global changes not only requires water quality data, but also information about the ecological context of individual water bodies across broad spatial extents. Because lake water quality is usually sampled in limited geographic regions, often for limited time periods, assessing the environmental controls of water quality requires compilation of many data sets across broad regions and across time into an integrated database. LAGOS-NE accomplishes this goal for lakes in the northeastern-most 17 US states.LAGOS-NE contains data for 51 101 lakes and reservoirs larger than 4 ha in 17 lake-rich US states. The database includes 3 data modules for: lake location and physical characteristics for all lakes; ecological context (i.e., the land use, geologic, climatic, and hydrologic setting of lakes) for all lakes; and in situ measurements of lake water quality for a subset of the lakes from the past 3 decades for approximately 2600-12 000 lakes depending on the variable. The database contains approximately 150 000 measures of total phosphorus, 200 000 measures of chlorophyll, and 900 000 measures of Secchi depth. The water quality data were compiled from 87 lake water quality data sets from federal, state, tribal, and non-profit agencies, university researchers, and citizen scientists. This database is one of the largest and most comprehensive databases of its type because it includes both in situ measurements and ecological context data. Because ecological context can be used to study a variety of other questions about lakes, streams, and wetlands, this database can also be used as the foundation for other studies of freshwaters at broad spatial and ecological scales.

Creating multithemed ecological regions for macroscale ecology: Testing a flexible, repeatable, and accessible clustering method.

Understanding broad-scale ecological patterns and processes often involves accounting for regional-scale heterogeneity. A common way to do so is to include ecological regions in sampling schemes and empirical models. However, most existing ecological regions were developed for specific purposes, using a limited set of geospatial features and irreproducible methods. Our study purpose was to: (1) describe a method that takes advantage of recent computational advances and increased availability of regional and global data sets to create customizable and reproducible ecological regions, (2) make this algorithm available for use and modification by others studying different ecosystems, variables of interest, study extents, and macroscale ecology research questions, and (3) demonstrate the power of this approach for the research question-How well do these regions capture regional-scale variation in lake water quality? To achieve our purpose we: (1) used a spatially constrained spectral clustering algorithm that balances geospatial homogeneity and region contiguity to create ecological regions using multiple terrestrial, climatic, and freshwater geospatial data for 17 northeastern U.S. states (~1,800,000 km2); (2) identified which of the 52 geospatial features were most influential in creating the resulting 100 regions; and (3) tested the ability of these ecological regions to capture regional variation in water nutrients and clarity for ~6,000 lakes. We found that: (1) a combination of terrestrial, climatic, and freshwater geospatial features influenced region creation, suggesting that the oft-ignored freshwater landscape provides novel information on landscape variability not captured by traditionally used climate and terrestrial metrics; and (2) the delineated regions captured macroscale heterogeneity in ecosystem properties not included in region delineation-approximately 40% of the variation in total phosphorus and water clarity among lakes was at the regional scale. Our results demonstrate the usefulness of this method for creating customizable and reproducible regions for research and management applications.

Building a multi-scaled geospatial temporal ecology database from disparate data sources: fostering open science and data reuse.

Although there are considerable site-based data for individual or groups of ecosystems, these datasets are widely scattered, have different data formats and conventions, and often have limited accessibility. At the broader scale, national datasets exist for a large number of geospatial features of land, water, and air that are needed to fully understand variation among these ecosystems. However, such datasets originate from different sources and have different spatial and temporal resolutions. By taking an open-science perspective and by combining site-based ecosystem datasets and national geospatial datasets, science gains the ability to ask important research questions related to grand environmental challenges that operate at broad scales. Documentation of such complicated database integration efforts, through peer-reviewed papers, is recommended to foster reproducibility and future use of the integrated database. Here, we describe the major steps, challenges, and considerations in building an integrated database of lake ecosystems, called LAGOS (LAke multi-scaled GeOSpatial and temporal database), that was developed at the sub-continental study extent of 17 US states (1,800,000 km(2)). LAGOS includes two modules: LAGOSGEO, with geospatial data on every lake with surface area larger than 4 ha in the study extent (~50,000 lakes), including climate, atmospheric deposition, land use/cover, hydrology, geology, and topography measured across a range of spatial and temporal extents; and LAGOSLIMNO, with lake water quality data compiled from ~100 individual datasets for a subset of lakes in the study extent (~10,000 lakes). Procedures for the integration of datasets included: creating a flexible database design; authoring and integrating metadata; documenting data provenance; quantifying spatial measures of geographic data; quality-controlling integrated and derived data; and extensively documenting the database. Our procedures make a large, complex, and integrated database reproducible and extensible, allowing users to ask new research questions with the existing database or through the addition of new data. The largest challenge of this task was the heterogeneity of the data, formats, and metadata. Many steps of data integration need manual input from experts in diverse fields, requiring close collaboration.

Cyanobacteria dominance influences resource use efficiency and community turnover in phytoplankton and zooplankton communities.

Freshwater biodiversity loss potentially disrupts ecosystem services related to water quality and may negatively impact ecosystem functioning and temporal community turnover. We analysed a data set containing phytoplankton and zooplankton community data from 131 lakes through 9 years in an agricultural region to test predictions that plankton communities with low biodiversity are less efficient in their use of limiting resources and display greater community turnover (measured as community dissimilarity). Phytoplankton resource use efficiency (RUE = biomass per unit resource) was negatively related to phytoplankton evenness (measured as Pielou's evenness), whereas zooplankton RUE was positively related to phytoplankton evenness. Phytoplankton and zooplankton RUE were high and low, respectively, when Cyanobacteria, especially Microcystis sp., dominated. Phytoplankton communities displayed slower community turnover rates when dominated by few genera. Our findings, which counter findings of many terrestrial studies, suggest that Cyanobacteria dominance may play important roles in ecosystem functioning and community turnover in nutrient-enriched lakes.

Watershed sediment losses to lakes accelerating despite agricultural soil conservation efforts.

Agricultural soil loss and deposition in aquatic ecosystems is a problem that impairs water quality worldwide and is costly to agriculture and food supplies. In the US, for example, billions of dollars have subsidized soil and water conservation practices in agricultural landscapes over the past decades. We used paleolimnological methods to reconstruct trends in sedimentation related to human-induced landscape change in 32 lakes in the intensively agricultural region of the Midwestern United States. Despite erosion control efforts, we found accelerating increases in sediment deposition from erosion; median erosion loss since 1800 has been 15.4 tons ha(-1). Sediment deposition from erosion increased >6-fold, from 149 g m(-2) yr(-1) in 1850 to 986 g m(-2) yr(-1) by 2010. Average time to accumulate one mm of sediment decreased from 631 days before European settlement (ca. 1850) to 59 days mm(-1) at present. Most of this sediment was deposited in the last 50 years and is related to agricultural intensification rather than land clearance or predominance of agricultural lands. In the face of these intensive agricultural practices, traditional soil conservation programs have not decelerated downstream losses. Despite large erosion control subsidies, erosion and declining water quality continue, thus new approaches are needed to mitigate erosion and water degradation.