Partitioning macroscale and microscale ecological processes using covariate-driven non-stationary spatial models.

Ecological inference requires integrating information across scales. This integration creates a complex spatial dependence structure that is most accurately represented by fully non-stationary models. However, ecologists rarely use these models because they are difficult to estimate and interpret. Here, we facilitate the use of fully non-stationary models in ecology by improving the interpretability of a recently developed non-stationary model and applying it to improve our understanding of the spatial processes driving lake eutrophication. We reformulated a model that incorporates non-stationary correlation by adding environmental predictors to the covariance function, thereby building on the intuition of mean regression. We created ellipses to visualize how data at a given site correlate with their surroundings (i.e., the range and directionality of underlying spatial processes). We applied this model to describe the spatial dependence structure of variables related to lake eutrophication across two different regions: a Midwestern United States region with highly agricultural landscapes, and a Northeastern United States region with heterogeneous land use. For the Midwest, increases in forest cover increased the homogeneity of the residual spatial structure of total phosphorus, indicating that macroscale processes dominated this nutrient's spatial structure. Conversely, high forest cover and baseflow reduced the spatial homogeneity of chlorophyll a residuals, indicating that microscale processes dominated for chlorophyll a in the Midwest. In the Northeast, increases in urban land use and baseflow decreased the homogeneity of phosphorus concentrations indicating the dominance of microscale processes, but none of our covariates were strongly associated with the residual spatial structure of chlorophyll a. Our model showed that the spatial dependence structure of environmental response variables shifts across space. It also helped to explain this structure using ecologically relevant covariates from different scales whose effects can be interpreted intuitively. This provided novel insight into the processes that lead to eutrophication, a complex and pervasive environmental issue.

Unexpected stasis in a changing world: Lake nutrient and chlorophyll trends since 1990.

The United States (U.S.) has faced major environmental changes in recent decades, including agricultural intensification and urban expansion, as well as changes in atmospheric deposition and climate-all of which may influence eutrophication of freshwaters. However, it is unclear whether or how water quality in lakes across diverse ecological settings has responded to environmental change. We quantified water quality trends in 2913 lakes using nutrient and chlorophyll (Chl) observations from the Lake Multi-Scaled Geospatial and Temporal Database of the Northeast U.S. (LAGOS-NE), a collection of preexisting lake data mostly from state agencies. LAGOS-NE was used to quantify whether lake water quality has changed from 1990 to 2013, and whether lake-specific or regional geophysical factors were related to the observed changes. We modeled change through time using hierarchical linear models for total nitrogen (TN), total phosphorus (TP), stoichiometry (TN:TP), and Chl. Both the slopes (percent change per year) and intercepts (value in 1990) were allowed to vary by lake and region. Across all lakes, TN declined at a rate of 1.1% year-1 , while TP, TN:TP, and Chl did not change. A minority (7%-16%) of individual lakes had changing nutrients, stoichiometry, or Chl. Of those lakes that changed, we found differences in the geospatial variables that were most related to the observed change in the response variables. For example, TN and TN:TP trends were related to region-level drivers associated with atmospheric deposition of N; TP trends were related to both lake and region-level drivers associated with climate and land use; and Chl trends were found in regions with high air temperature at the beginning of the study period. We conclude that despite large environmental change and management efforts over recent decades, water quality of lakes in the Midwest and Northeast U.S. has not overwhelmingly degraded or improved.

Lake nutrient stoichiometry is less predictable than nutrient concentrations at regional and sub-continental scales.

Production in many ecosystems is co-limited by multiple elements. While a known suite of drivers associated with nutrient sources, nutrient transport, and internal processing controls concentrations of phosphorus (P) and nitrogen (N) in lakes, much less is known about whether the drivers of single nutrient concentrations can also explain spatial or temporal variation in lake N:P stoichiometry. Predicting stoichiometry might be more complex than predicting concentrations of individual elements because some drivers have similar relationships with N and P, leading to a weak relationship with their ratio. Further, the dominant controls on elemental concentrations likely vary across regions, resulting in context dependent relationships between drivers, lake nutrients and their ratios. Here, we examine whether known drivers of N and P concentrations can explain variation in N:P stoichiometry, and whether explaining variation in stoichiometry differs across regions. We examined drivers of N:P in ~2,700 lakes at a sub-continental scale and two large regions nested within the sub-continental study area that have contrasting ecological context, including differences in the dominant type of land cover (agriculture vs. forest). At the sub-continental scale, lake nutrient concentrations were correlated with nutrient loading and lake internal processing, but stoichiometry was only weakly correlated to drivers of lake nutrients. At the regional scale, drivers that explained variation in nutrients and stoichiometry differed between regions. In the Midwestern U.S. region, dominated by agricultural land use, lake depth and the percentage of row crop agriculture were strong predictors of stoichiometry because only phosphorus was related to lake depth and only nitrogen was related to the percentage of row crop agriculture. In contrast, all drivers were related to N and P in similar ways in the Northeastern U.S. region, leading to weak relationships between drivers and stoichiometry. Our results suggest ecological context mediates controls on lake nutrients and stoichiometry. Predicting stoichiometry was generally more difficult than predicting nutrient concentrations, but human activity may decouple N and P, leading to better prediction of N:P stoichiometry in regions with high anthropogenic activity.