Jacob A. Zwart

The importance of lake-specific characteristics for water quality across the continental United States.

Lake water quality is affected by local and regional drivers, including lake physical characteristics, hydrology, landscape position, land cover, land use, geology, and climate. Here, we demonstrate the utility of hypothesis testing within the landscape limnology framework using a random forest algorithm on a national-scale, spatially explicit data set, the United States Environmental Protection Agencys 2007 National Lakes Assessment. For 1026 lakes, we tested the relative importance of water quality drivers across spatial scales, the importance of hydrologic connectivity in mediating water quality drivers, and how the importance of both spatial scale and connectivity differ across response variables for five important in-lake water quality metrics (total phosphorus, total nitrogen, dissolved organic carbon, turbidity, and conductivity). By modeling the effect of water quality predictors at different spatial scales, we found that lake-specific characteristics (e.g., depth, sediment area-to-volume ratio) were important for explaining water quality (54-60% variance explained), and that regionalization schemes were much less effective than lake specific metrics (28-39% variance explained). Basin-scale land use and land cover explained between 45-62% of variance, and forest cover and agricultural land uses were among the most important basin-scale predictors. Water quality drivers did not operate independently; in some cases, hydrologic connectivity (the presence of upstream surface water features) mediated the effect of regional-scale drivers. For example, for water quality in lakes with upstream lakes, regional classification schemes were much less effective predictors than lake-specific variables, in contrast to lakes with no upstream lakes or with no surface inflows. At the scale of the continental United States, conductivity was explained by drivers operating at larger spatial scales than for other water quality responses. The current regulatory practice of using regionalization schemes to guide water quality criteria could be improved by consideration of lake-specific characteristics, which were the most important predictors of water quality at the scale of the continental United States. The spatial extent and high quality of contextual data available for this analysis makes this work an unprecedented application of landscape limnology theory to water quality data. Further, the demonstrated importance of lake morphology over other controls on water quality is relevant to both aquatic scientists and managers.

Phytoplankton traits predict ecosystem function in a global set of lakes

Predicting ecosystem function from environmental conditions is a central goal of ecosystem ecology. However, many traditional ecosystem models are tailored for specific regions or ecosystem types, requiring several regional models to predict the same function. Alternatively, trait-based approaches have been effectively used to predict community structure in both terrestrial and aquatic environments and ecosystem function in a limited number of terrestrial examples. Here, we test the efficacy of a trait-based model in predicting gross primary production (GPP) in lake ecosystems. We incorporated data from >1000 United States lakes along with laboratory-generated phytoplankton trait data to build a trait-based model of GPP and then validated the model with GPP observations from a separate set of globally distributed lakes. The trait-based model performed as well as or outperformed two ecosystem models both spatially and temporally, demonstrating the efficacy of trait-based models for predicting ecosystem function over a range of environmental conditions.

The Influence of Hydrologic Residence Time on Lake Carbon Cycling Dynamics Following Extreme Precipitation Events

The frequency and magnitude of extreme events are expected to increase in the future, yet little is known about effects of such events on ecosystem structure and function. We examined how extreme precipitation events affect exports of terrestrial dissolved organic carbon (t-DOC) from watersheds to lakes as well as in-lake heterotrophy in three north-temperate lakes. Extreme precipitation events induced large influxes of t-DOC to our lakes, accounting for 45–58% of the seasonal t-DOC load. These large influxes of t-DOC influenced lake metabolism, resulting in lake net heterotrophy following 67% of the extreme precipitation events across all lakes. Hydrologic residence time (HRT) was negatively related to t-DOC load and heterotrophy; lakes with short HRT had higher t-DOC loads and greater net heterotrophy. The fraction of t-DOC mineralized within each lake following extreme precipitation events generally exhibited a positive relationship with lake HRT, similar to the previous studies of fractions mineralized at annual and supra-annual time scales. Event-associated turnover rate of t-DOC was higher than what is typically reported from laboratory studies and modeling exercises and was also negatively related to lake HRT. This study demonstrates that extreme precipitation events are ‘hot moments’ of carbon load, export, and turnover in lakes and that lake-specific characteristics (for example, HRT) interact with climatic patterns to set rates of important lake carbon fluxes.

Model‐Data Fusion to Test Hypothesized Drivers of Lake Carbon Cycling Reveals Importance of Physical Controls

Oleksandra Hararuk1˒4 , Jacob A. Zwart2, Stuart E. Jones2, Yves Prairie3, and Christopher T. Solomon4 1Department of Natural Resource Sciences, McGill University, Montréal, QC, H9X 3V9, Canada 2Department of Biological Sciences, University of Notre Dame, Notre Dame, IN, 46556, USA 3Départment des sciences biologiques, Université du Québec à Montréal, Montréal, QC, H3C 3P8, Canada 4 Cary Institute of Ecosystem Studies, Millbrook, NY, 12545, USA Corresponding author: Oleksandra Hararuk (ohararuk@gmail.com)

Spatially Explicit, Regional‐Scale Simulation of Lake Carbon Fluxes

Lakes are areas of intense biogeochemical processing in the landscape, contributing significantly to the global carbon cycle despite their small areal coverage. However, current large-scale estimates of lake biogeochemical fluxes are all generated by multiplying a mean observed areal rate by regional or global lake surface area, which ignores important heterogeneous spatial and temporal processes that regulate lake carbon cycling. We have developed a process-based model that integrates core scientific knowledge in hydrology, biogeochemistry, and ecology that is specifically designed to be applied over large geographic regions to hindcast or forecast regional lake carbon fluxes. We used our model to simulate daily carbon fluxes and pools for 3,675 lakes in the Northern Highlands Lake District from 1980–2010 and produced spatial and seasonal patterns consistent with observations. Variabilities in lake carbon fluxes were well predicted by relatively simple hydrologic metrics, such as the fraction of hydrologic export as evaporation (FHEE). Overall, lakes with a high FHEE processed a greater percentage of carbon inputs in the simulations than lakes with a low FHEE, but low-FHEE lakes ultimately processed more total carbon because of greater carbon inputs. Large lakes with low FHEE and high external loading of dissolved inorganic carbon contributed most to total CO2 emissions for the Northern Highlands Lake District, and our model estimated that 78% of total CO2 emissions from lakes to the atmosphere originated from external loads of dissolved inorganic carbon. By better characterizing the unique biogeochemical processes for each individual lake, regional estimates of carbon fluxes are more accurately determined.

Light climate and dissolved organic carbon concentration influence species-specific changes in fish zooplanktivory

Abstract Dissolved organic carbon (DOC) in lakes reduces light penetration and limits fish production in low nutrient lakes, reportedly via reduced primary and secondary production. Alternatively, DOC and light reductions could influence fish by altering their visual feeding. Previous studies report mixed effects of DOC on feeding rates of zooplanktivorous fish, but most investigators tested effects of a single concentration of DOC against clear-water, turbid, or algal treatments. We used a controlled laboratory study to quantify the effects of a DOC gradient (3–19 mg L−1) on average light climate and the zooplankton feeding rate of 3 common, north temperate fishes. Light availability, which was inversely related to DOC concentration, had a positive and linear effect on zooplankton consumption by juvenile largemouth bass (Micropterus salmoides) and bluegill (Lepomis macrochirus), explaining 22% and 28% of the variation in consumption, respectively. By contrast, zooplankton feeding rates by fathead minnow (Pimephales promelas) were best predicted by a nonlinear, negative influence of light (R2 = 0.13). In bluegill feeding trials we found a general trend for positive selection of larger zooplankton (Cladocera and Chaoboridae); however, the light climate did not influence the selection of prey type. Largemouth bass selected for larger-bodied zooplankton, with weak evidence that selectivity for large Cladocera changed from negative to neutral selection based on electivity values across the light gradient. Our results suggest that the effect of DOC on the light climate of lakes may directly influence fish zooplanktivory and that this influence may vary among fish species.

Integrated, Regional-Scale Hydrologic Modeling of Inland Lakes

Inland lakes constitute an important global freshwater resource and are often defining features of local and regional landscapes. While coupled surface water (SW) and groundwater (GW) models are increasingly available, there is a clear need for spatially explicit yet computationally parsimonious modeling frameworks to explore the impacts of climate, land use, and other drivers on lake hydrologic and biogeochemical processes. To address this need, we developed a new method to simulate daily water budgets for many individual lakes at large spatial scales. By integrating SW, GW, and lake water budget models in a simple manner, we created a modeling framework capable of simulating the historical and future hydrologic dynamics of lakes with varying hydrologic characteristics. By extension, the model output enables ecological modeling in response to hydrologic drivers. As a case study, we applied the model to a large, lake-rich region in northern Wisconsin and Michigan, simulating daily water budgets for nearly 4,000 lakes over a 36-year period. Despite minimal calibration efforts, our simulated results compared reasonably well with observations and more sophisticated modeling approaches. Our integrated modeling requires very limited information, can be run on readily available computer resources, such as a desktop PC, and can be applied at regional, continental, or global scales, where necessary model setup and forcing data are available. (

A Framework for Understanding Variation in Pelagic Gross Primary Production of Lake Ecosystems

Light and nutrient availability are key physiological constraints for primary production. Widespread environmental changes are causing variability in loads of terrestrial dissolved organic carbon (DOC) and nutrients from watersheds to lakes, contributing to simultaneous changes in both light and nutrient supply. Experimental evidence highlights the potential for these watershed loads to create complex and context-dependent responses of within-lake primary production; however, the field lacks a predictive model to investigate these responses. We embedded a well-established physiological model of phytoplankton growth within an ecosystem model of nutrient and DOC supply to assess how simultaneous changes in DOC and nutrient loads could impact pelagic primary production in lakes. The model generated a unimodal relationship between GPP and DOC concentration when loads of DOC and nutrients were tightly correlated across space or time. In this unimodal relationship, the magnitude of the peak GPP was primarily determined by the DOC-to-nutrient ratio of the load, and the location of the peak along the DOC axis was primarily determined by lake area. Greater nutrient supply relative to DOC load contributed to greater productivity, and larger lake area increased light limitation for primary producers at a given DOC concentration, owing to the positive relationship between lake area and epilimnion depth. When loads of DOC and nutrients were not tightly correlated in space or time, the model generated a wedge-shaped pattern between GPP and DOC, consistent with spatial surveys from a global set of lakes. Our model is thus capable of unifying the diversity of empirically observed spatial and temporal responses of lake productivity to DOC and mineral nutrient supply presented in the literature, and provides qualitative predictions for how lake pelagic primary productivity may respond to widespread environmental changes.