Assessing degradation and recovery pathways in lakes impacted by eutrophication using the sediment record

Abstract

Efforts to restore enriched lakes have increased yet there remains uncertainty about whether restoration targets can be achieved and over what timescale. Paleoecological techniques, principally diatom analyses, were used to examine the degree of impact and recovery in 12 European lakes subject to eutrophication and subsequent reduction in nutrient loading. Dissimilarity scores showed that all sites experienced progressive deviation from the reference sample (core bottom) prior to nutrient reduction, and principal curves indicated gradual compositional change with enrichment. When additive models were applied to the latter, the changes were statistically significant in 9 of the 12 sites. Shifts in diatom composition following reduction in nutrient loading were more equivocal, with a reversal toward the reference flora seen only in four of the deep lakes and one of the shallow lakes. Of these, only two were significant (Lake Bled and Mjøsa). Alternative nutrient sources seem to explain the lack of apparent recovery in the other deep lakes. In three shallow lakes diatom assemblages were replaced by a community associated with lower productivity but not the one seen prior to enrichment. Internal loading and top down control may influence recovery in shallow lakes and climate change may have confounded recovery in several of the study sites. Hence, ecosystem recovery is not simply a reversal of the degradation pathway and may take several decades to complete or, for some lakes, may not take place at all. By assessing ecological change over a decadal to centennial timescale, the study highlights the important role that paleolimnology can play in establishing a benchmark against which managers can evaluate the degree to which their restoration efforts are successful.

Seeking alternative stable states in a deep lake

“…The results on DCA1 underlined the necessity to consider the possibility of multiforcings acting on ecological dynamics when evaluating restoration trajectories (e.g. Bennion et al, 2015). DCA2 instead gave a more precise picture of the nutrient-driven changes.…”

“…Generally, in deep lakes, the delayed and incomplete recovery of water transparency is regarded as an outcome of additional human disturbances (including climate warming, Alric et al, 2013;Jeppesen et al, 2005;Perga et al, 2015) driving the system to a new ecological trajectory (Battarbee, John Anderson, Jeppesen, & Leavitt, 2005;Bennion, Simpson, & Goldsmith, 2015). Indeed, climate change can impact lake ecosystems through several direct and indirect pathways, including a lengthening of the stratification period and enhancement of hypoxia, both promoting P release from the sediment, changes in water level or nutrient loadings, or temperature-induced changes in biotic interactions .…”

Modelling Palaeoecological Time Series Using Generalised Additive Models

“…A pragmatic although inelegant approach that has been used to estimate trends in multivariate palaeoecological data is to first summarise the response data using an unconstrained ordination via a PCA, CA, or principal curve and then fit separate GAM models to the site (sample) scores of the first few ordination axes or principal curve (Bennion et al, 2015;Beck et al, 2018). Whilst this two-step approach is relatively easy to implement and builds on approaches that palaeoecologists already use to summarise multivariate stratigraphic data, it is best thought of as modelling changes in abundance or relative composition at the community level.…”

Corals sustain growth but not skeletal density across the Florida Keys Reef Tract despite ongoing warming

“…The smoothing 222 basis (k = 25) was selected following the protocol recommended by Wood (2017), whereby k was 223 increased progressively (k = 5, 10, 15, 20, 25) until the effective degrees of freedom stabilized at 224 a value sufficiently lower than k -1. Once the models were fitted to the data, time intervals of 225 significant change were computed using the first derivative of the fitted trend spline, following the 226 methods of Bennion, Simpson, and Goldsmith (2015). In short, a finite differences approximation 227 of the first derivative is calculated at fixed time points along the model prediction with an 228 associated 95% confidence interval.…”