Time‐varying responses of lake metabolism to light and temperature

Abstract

Light is a primary driver of lake ecosystem metabolism, and the dependence of primary production on light is often quantified as a photosynthesis‐irradiance or “P‐I” curve. The parameters of the P‐I curve (e.g., the maximum primary production when light is in excess) can change through time due to a variety of biological factors (e.g., changes in biomass or community composition), which themselves are subject to external drivers (e.g., herbivory or nutrient availability). However, the relative contribution of variation in the P‐I curve to overall ecosystem metabolism is largely unknown. I developed a statistical model of ecosystem metabolism with time‐varying parameters governing the P‐I curve, while also accounting for the influence of temperature. I parameterized the model with dissolved oxygen time series spanning six summers from Lake Mývatn, a shallow eutrophic lake in northern Iceland with large temporal variability in ecosystem metabolism. All of the estimated parameters of the P‐I curve varied substantially through time. The sensitivity of primary production to light under light‐limiting conditions was particularly variable (>15‐fold) and had a compensatory relationship with ambient light levels. However, the 3.5‐fold variation in the maximum potential primary production made the largest contribution to variation in ecosystem metabolism, accounting for around 90% of the variance in net ecosystem production. Much of the variation in maximum primary production was attributable to cyanobacterial blooms, which occur in some but not all years in Mývatn. Overall, these results illustrate how changes in the P‐I curve contribute substantially to temporal variation in lake ecosystem metabolism.

Hydroclimate Variability Affects Habitat‐Specific (Open Water and Littoral) Lake Metabolism

“…It is often difficult to identify statistical differences among different model formulations when fit to real data (Aalderink & Jovin, 1997) suggesting that the simple model will perform similarly to its more complex counterparts. We allowed P max , I opt , and the respiration coefficient (see Phillips, 2020 for model formulation) to vary through time at a daily time scale. The degree of autocorrelation in the parameters through time was constrained by hierarchical variance parameters in the random walk components of the model.…”

Rapid adaptive responses of rosette‐type macrophyte Vallisneria natans juveniles to varying water depths: The role of leaf trait plasticity

“…In this study, we used the average daily light integral (DLI, mol photons m −2 d −1 ) as the quantifier of the light intensity experienced by the plants, since DLI rather than instantaneous light intensity relates better to the plants’ overall experienced light regime (Poorter et al., 2019 ). To calculate the DLI, the light measurements from the logger were converted to photosynthetically active radiation (PAR, measured in μmol photons m −2 s −1 ) based on a standard relationship (see Phillips, 2019 ; Thimijan & Heins, 1983 ), and the value of DLI was obtained by integrating the PAR over a day (Figure A2 a,b). Water chemical parameters were measured at each depth every week.…”

Aquatic ecosystem metabolism as a tool in environmental management

“…It is expected that short‐term (i.e., daily) variation is for the most part driven by changes in the primary physical drivers of biochemical reactions via light and temperature and that in the longer‐term biological structure of an aquatic ecosystem is driven by changes in both the primary and secondary drivers (Kendrick & Huryn, 2015; Ulseth, Bertuzzo, Singer, Schelker, & Battin, 2018) with resulting shifts in the biological communities. Current ecosystem metabolism models can account for the short term variation by estimating standardized metrics of photosynthesis to irradiance (Beaulieu et al, 2013; Holtgrieve et al, 2010; Phillips, 2020) and temperature sensitivity coefficients of ER (Jankowski & Schindler, 2019; Phillips, 2020; Song et al, 2018). Only recently have metabolism models been developed to explicitly consider longer‐term dynamics and latent biological variables such as autotrophic and heterotrophic biomass (Segatto et al, 2020).…”