Resilience is the ability of ecosystems to maintain function while experiencing perturbation. Globally, forests are experiencing disturbances of unprecedented quantity, type, and magnitude that may diminish resilience. Early warning signals are statistical properties of data whose increase over time may provide insights into decreasing resilience, but there have been few applications to forests. We quantified four early warning signals (standard deviation, lag-1 autocorrelation, skewness, and kurtosis) across detrended time series of multiple ecosystem state variables at the Hubbard Brook Experimental Forest, New Hampshire, USA and analyzed how these signals have changed over time. Variables were collected over periods from 25 to 55 years from both experimentally manipulated and reference areas and were aggregated to annual timesteps for analysis. Long-term (>50 year) increases in early warning signals of stream calcium, a key biogeochemical variable at the site, illustrated declining resilience after decades of acid deposition, but only in watersheds that had previously been harvested. Trends in early warning signals of stream nitrate, a critical nutrient and water pollutant, likewise exhibited symptoms of declining resilience but in all watersheds. Temporal trends in early warning signals of some of groups of trees, insects, and birds also indicated changing resilience, but this pattern differed among, and even within, groups. Overall, ∼60% of early warning signals analyzed indicated decreasing resilience. Most of these signals occurred in skewness and kurtosis, suggesting ‘flickering’ behavior that aligns with emerging evidence of the forest transitioning into an oligotrophic condition. The other ∼40% of early warning signals indicated increasing or unchanging resilience. Interpretation of early warning signals in the context of system specific knowledge is therefore essential. They can be useful indicators for some key ecosystem variables; however, uncertainties in other variables highlight the need for further development of these tools in well-studied, long-term research sites.
Regime shifts have large consequences for ecosystems and the services they provide. However, understanding the potential for, causes of, proximity to, and thresholds for regime shifts in nearly all settings is difficult. Generic statistical indicators of resilience have been proposed and studied in a wide range of ecosystems as a method to detect when regime shifts are becoming more likely without direct knowledge of underlying system dynamics or thresholds. These early warning statistics (EWS) have been studied separately but there have been few examples that directly compare temporal and spatial EWS in ecosystem‐scale empirical data. To test these methods, we collected high‐frequency time series and high‐resolution spatial data during a whole‐lake fertilization experiment while also monitoring an adjacent reference lake. We calculated two common EWS, standard deviation and autocorrelation, in both time series and spatial data to evaluate their performance prior to the resulting algal bloom. We also applied the quickest detection method to generate binary alarms of resilience change from temporal EWS. One temporal EWS, rolling window standard deviation, provided advanced warning in most variables prior to the bloom, showing trends and between‐lake patterns consistent with theory. In contrast, temporal autocorrelation and both measures of spatial EWS (spatial SD, Moran's
- NSF-PAR ID:
- 10371137
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Ecological Applications
- Volume:
- 32
- Issue:
- 5
- ISSN:
- 1051-0761
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
more » « less
Abstract -
null (Ed.)Spurious power consumption data reported from compromised meters controlled by organized adversaries in the Advanced Metering Infrastructure (AMI) may have drastic consequences on a smart grid’s operations. While existing research on data falsification in smart grids mostly defends against isolated electricity theft, we introduce a taxonomy of various data falsification attack types, when smart meters are compromised by organized or strategic rivals. To counter these attacks, we first propose a coarse-grained and a fine-grained anomaly-based security event detection technique that uses indicators such as deviation and directional change in the time series of the proposed anomaly detection metrics to indicate: (i) occurrence, (ii) type of attack, and (iii) attack strategy used, collectively known as attack context . Leveraging the attack context information, we propose three attack response metrics to the inferred attack context: (a) an unbiased mean indicating a robust location parameter; (b) a median absolute deviation indicating a robust scale parameter; and (c) an attack probability time ratio metric indicating the active time horizon of attacks. Subsequently, we propose a trust scoring model based on Kullback-Leibler (KL) divergence, that embeds the appropriate unbiased mean, the median absolute deviation, and the attack probability ratio metric at runtime to produce trust scores for each smart meter. These trust scores help classify compromised smart meters from the non-compromised ones. The embedding of the attack context, into the trust scoring model, facilitates accurate and rapid classification of compromised meters, even under large fractions of compromised meters, generalize across various attack strategies and margins of false data. Using real datasets collected from two different AMIs, experimental results show that our proposed framework has a high true positive detection rate, while the average false alarm and missed detection rates are much lesser than 10% for most attack combinations for two different real AMI micro-grid datasets. Finally, we also establish fundamental theoretical limits of the proposed method, which will help assess the applicability of our method to other domains.more » « less
-
more » « less
Abstract Successful conservation of ecosystems exhibiting alternative stable states requires tools to accurately classify states and quantify state transition risk. Methods that utilize early warning signals are promising approaches for helping managers anticipate impending state transitions, but they require high‐resolution temporal or spatial data for individual sites, and they do not directly implicate causes or quantify their impacts on transition risk. There is need for a modelling approach that can assess state transition risk with lower resolution temporal data, identify drivers of state transitions and assess the impact of changes in these drivers on the persistence of ecosystem states.
We developed a novel, integrated modelling framework that (a) classifies states in a way that reflects the qualitative dynamics of catastrophic regime shifts, (b) estimates state transition probabilities and (c) uses annual survey data to identify top predictors of state transitions and quantify their effects on transition risk. We applied our model to short time series from 123 shallow lakes that exhibit clear‐ and turbid‐water alternative stable states.
We found that clear lakes were more likely to transition to the turbid state as total phosphorus levels increased or occurrence of submerged vegetation decreased. Additionally, increases in planktivorous or benthivorous fish biomass elevated transition risk. Too few turbid‐to‐clear transitions were observed to identify predictors of transitions in this direction.
Synthesis and applications . Our study will inform conservation and management strategies for ecosystems exhibiting alternative stable states by providing a new tool to accurately classify states, compare state transition risk among sites based on resilience and system perturbations, and identify key variables to target to prevent undesirable transitions. Although we focus on shallow lakes as a case study for our modelling approach, we emphasize that our framework is applicable to any ecosystem known to exhibit alternative stable states. -
Rudi, Knut (Ed.)ABSTRACT Cyanobacterial harmful algal blooms (cyanoHABs) degrade freshwater ecosystems globally. Microcystis aeruginosa often dominates cyanoHABs and produces microcystin (MC), a class of hepatotoxins that poses threats to human and animal health. Microcystin toxicity is influenced by distinct structural elements across a diversity of related molecules encoded by variant mcy operons. However, the composition and distribution of mcy operon variants in natural blooms remain poorly understood. Here, we characterized the variant composition of mcy genes in western Lake Erie Microcystis blooms from 2014 and 2018. Sampling was conducted across several spatial and temporal scales, including different bloom phases within 2014, extensive spatial coverage on the same day (2018), and frequent, autonomous sampling over a 2-week period (2018). Mapping of metagenomic and metatranscriptomic sequences to reference sequences revealed three Microcystis mcy genotypes: complete (all genes present [ mcyA–J ]), partial (truncated mcyA , complete mcyBC , and missing mcyD–J ), and absent (no mcy genes). We also detected two different variants of mcyB that may influence the production of microcystin congeners. The relative abundance of these genotypes was correlated with pH and nitrate concentrations. Metatranscriptomic analysis revealed that partial operons were, at times, the most abundant genotype and expressed in situ , suggesting the potential biosynthesis of truncated products. Quantification of genetic divergence between genotypes suggests that the observed strains are the result of preexisting heterogeneity rather than de novo mutation during the sampling period. Overall, our results show that natural Microcystis populations contain several cooccurring mcy genotypes that dynamically shift in abundance spatiotemporally via strain succession and likely influence the observed diversity of the produced congeners. IMPORTANCE Cyanobacteria are responsible for producing microcystins (MCs), a class of potent and structurally diverse toxins, in freshwater systems around the world. While microcystins have been studied for over 50 years, the diversity of their chemical forms and how this variation is encoded at the genetic level remain poorly understood, especially within natural populations of cyanobacterial harmful algal blooms (cyanoHABs). Here, we leverage community DNA and RNA sequences to track shifts in mcy genes responsible for producing microcystin, uncovering the relative abundance, expression, and variation of these genes. We studied this phenomenon in western Lake Erie, which suffers annually from cyanoHAB events, with impacts on drinking water, recreation, tourism, and commercial fishing.more » « less
-
Lakes are key ecosystems within the global biogeosphere. However, the bottom-up controls on the biological productivity of lakes, including surface temperature, ice phenology, nutrient loads and mixing regime, are increasingly altered by climate warming and land-use changes. To better understand the environmental drivers of lake productivity, we assembled a dataset on chlorophyll-a concentrations, as well as associated water quality parameters and surface solar irradiance, for temperate and cold-temperate lakes experiencing seasonal ice cover. We developed a method to identify periods of rapid algal growth from in situ chlorophyll-a time series data and applied it to measurements performed between 1964 and 2019 across 357 lakes, predominantly located north of 40°. Long-term trends show that the algal growth windows have been occurring earlier in the year, thus potentially extending the growing season and increasing the annual productivity of northern lakes. The dataset is also used to analyze the relationship between chlorophyll-a growth rates and solar irradiance. Lakes of higher trophic status exhibit a higher sensitivity to solar radiation, especially at moderate irradiance values during spring. The lower sensitivity of chlorophyll-a growth rates to solar irradiance in oligotrophic lakes likely reflects the dominant role of nutrient limitation. Chlorophyll-a growth rates are significantly influenced by light availability in spring but not in summer and fall, consistent with a switch to top-down control of summer and fall algal communities. The growth window dataset can be used to analyze trends in lake productivity across the northern hemisphere or at smaller, regional scales. We present some general trends in the data and encourage other researchers to use the open dataset for their own research questions.more » « less
