Shifting regimes and changing interactions in the Lake Washington, U.S.A., plankton community from 1962-1994

Modeling freshwater plankton community dynamics with static and dynamic interactions using graph convolution embedded long short-term memory

Given the frequent association between freshwater plankton and water quality degradation, several predictive models have been devised to understand and estimate their dynamics. However, the significance of biotic and abiotic interactions has been overlooked. In this study, we aimed to address the importance of the interaction term in predicting plankton community dynamics by applying graph convolution embedded long short-term memory networks (GC-LSTM) models, which can incorporate interaction terms as graph signals. Temporal graph series comprising plankton genera or environmental drivers as node features and their relationships for edge features from two distinct water bodies, a reservoir and a river, were utilized to develop these models. To assess the predictability, the performances of the GC-LSTM models on community dynamics were compared those of LSTM and GCN models at various lead times. Moreover, GNNExplainer was used to examine the global and local importance of the nodes and edges for all predictions and specific predictions, respectively. The GC-LSTM models outperformed the LSTM models, consistently showing higher prediction accuracy. Although all the models exhibited performance degradation at longer lead times, the GC-LSTM models consistently demonstrated better performance regarding each graph signal and plankton genus. GNNExplainer yielded interpretable explanations for important genera and interaction pairs among communities, revealing consistent importance patterns across different lead times at both global and local scales. These findings underscore the potential of the proposed modeling approach for forecasting community dynamics and emphasize the critical role of graph signals with interaction terms in plankton communities.

Body size and trophic position determine the outcomes of species invasions along temperature and productivity gradients

Species invasions are predicted to increase in frequency with global change, but quantitative predictions of how environmental filters and species traits influence the success and consequences of invasions for local communities are lacking. Here we investigate how invaders alter the structure, diversity and stability regime of simple communities across environmental gradients (habitat productivity, temperature) and community size structure. We simulate all three-species trophic modules (apparent and exploitative competition, trophic chain and intraguild predation). We predict that invasions most often succeed in warm and productive habitats and that successful invaders include smaller competitors, intraguild predators and comparatively small top predators. This suggests that species invasions and global change may facilitate the downsizing of food webs. Furthermore, we show that successful invasions leading to species substitutions rarely alter system stability, while invasions leading to increased diversity can destabilize or stabilize community dynamics depending on the environmental conditions and invader's trophic position.

Early warning signals have limited applicability to empirical lake data

Research aimed at identifying indicators of persistent abrupt shifts in ecological communities, a.k.a regime shifts, has led to the development of a suite of early warning signals (EWSs). As these often perform inaccurately when applied to real-world observational data, it remains unclear whether critical transitions are the dominant mechanism of regime shifts and, if so, which EWS methods can predict them. Here, using multi-trophic planktonic data on multiple lakes from around the world, we classify both lake dynamics and the reliability of classic and second generation EWSs methods to predict whole-ecosystem change. We find few instances of critical transitions, with different trophic levels often expressing different forms of abrupt change. The ability to predict this change is highly processing dependant, with most indicators not performing better than chance, multivariate EWSs being weakly superior to univariate, and a recent machine learning model performing poorly. Our results suggest that predictive ecology should start to move away from the concept of critical transitions, developing methods suitable for predicting resilience loss not limited to the strict bounds of bifurcation theory.

Disentangling the effects of phosphorus loading on food web stability in a large shallow lake

Excessive nutrient loads reduce ecosystem resilience, resulting in fundamental changes in ecosystem structure and function when exceeding a certain threshold. However, quantitative analysis of the processes by which nutrient loading affects ecosystem resilience requires further exploration. Food web stability is at the heart of ecosystem resilience. In this study, we simulated the dynamics of the food web under different phosphorus loads for Lake Baiyangdian using the PCLake model and calculated the food web stability. Our results showed that there was a good correspondence between the food web stability and ecosystem state response to phosphorus loads. This relationship confirmed that food web stability could be regarded as a signal for the state transition in a real lake ecosystem. Moreover, our estimates suggested that food web stability was influenced only by several functional groups and their interaction strength. Diatoms and zooplankton were the key functional groups that affected food web stability. Phosphorus loads alter the distribution of functional group biomass, which in turn affects energy delivery and, ultimately, the stability of the food web. Corresponding to functional groups, the interactions among zooplankton, diatoms and detritus had the greatest impact, and the interaction strength of the three was positively correlated with food web stability. Overall, our study explained that food-web stability was critical to characterize ecosystem resilience response to external disturbances and can be turned into a scientific tool for lake ecosystem management.

Regularizing priors for Bayesian VAR applications to large ecological datasets

Using multi-species time series data has long been of interest for estimating inter-specific interactions with vector autoregressive models (VAR) and state space VAR models (VARSS); these methods are also described in the ecological literature as multivariate autoregressive models (MAR, MARSS). To date, most studies have used these approaches on relatively small food webs where the total number of interactions to be estimated is relatively small. However, as the number of species or functional groups increases, the length of the time series must also increase to provide enough degrees of freedom with which to estimate the pairwise interactions. To address this issue, we use Bayesian methods to explore the potential benefits of using regularized priors, such as Laplace and regularized horseshoe, on estimating interspecific interactions with VAR and VARSS models. We first perform a large-scale simulation study, examining the performance of alternative priors across various levels of observation error. Results from these simulations show that for sparse matrices, the regularized horseshoe prior minimizes the bias and variance across all inter-specific interactions. We then apply the Bayesian VAR model with regularized priors to a output from a large marine food web model (37 species) from the west coast of the USA. Results from this analysis indicate that regularization improves predictive performance of the VAR model, while still identifying important inter-specific interactions.

Identifying biotic drivers of population dynamics in a benthic-pelagic community

Benthic species and communities are linked to pelagic zooplankton through life-stages encompassing both benthic and pelagic habitats and through a mutual dependency on primary producers as a food source. Many zooplankton taxa contribute to the sedimentary system as benthic eggs. Our main aim was to investigate the nature of the population level biotic interactions between and within these two seemingly independent communities, both dependent on the pelagic primary production, while simultaneously accounting for environmental drivers (salinity, temperature, and oxygen conditions). To this end, we applied multivariate autoregressive state-space models to long (1966-2007) time series of annual abundance data, comparing models with and without interspecific interactions, and models with and without environmental variables included. We were not able to detect any direct coupling between sediment-dwelling benthic taxa and pelagic copepods and cladocerans on the annual scale, but the most parsimonious model indicated that interactions within the benthic community are important. There were also positive residual correlations between the copepods and cladocerans potentially reflecting the availability of a shared resource or similar seasonal dependence, whereas both groups tended to correlate negatively with the zoobenthic taxa. The most notable single interaction within the benthic community was a tendency for a negative effect of Limecola balthica on the amphipods Monoporeia affinis and Pontoporeia femorata which can help explain the observed decrease in amphipods due to increased competitive interference.

Modeling the trophic impacts of invasive zooplankton in a highly invaded river

The lower Columbia River (Washington and Oregon, USA) has been heavily invaded by a large number of planktonic organisms including the invasive copepod Pseudodiaptomus forbesi and the planktonic juveniles of the invasive clam, Corbicula fluminea. In order to assess the ecological impacts of these highly abundant invaders, we developed a multivariate auto-regressive (MAR) model of food web dynamics based upon a 12-year time-series of plankton community and environmental data from the Columbia River. Our model results indicate that plankton communities in the lower Columbia River are strongly impacted by the copepod P. forbesi at multiple trophic levels. We observed different ecological effects across different life stages of P. forbesi, with nauplii negatively impacting ciliates and autotrophs, and copepodite stages negatively impacting Daphnia and cyclopoid copepods. Although juvenile C. fluminea were highly abundant in the summer and autumn of each year, our best fit MAR model did not show significant C. fluminea impacts. Our results illustrate the strong ecological impact that some zooplankton invaders may cause within rivers and estuarine systems, and highlight the need for further research on the feeding ecology of the planktonic life-stage of C. fluminea. Overall, our study demonstrates the manner in which long-term, high resolution data sets can be used to better understand the ecological impacts of invasive species among complex and highly dynamic communities.

Drivers shaping episodic and gradual changes in phytoplankton community succession: Taxonomic versus functional groups

Describing temporal changes in phytoplankton communities is complicated owing to (i) multivariate environmental drivers, (ii) inter-specific relationships, and (iii) various species. With long-term research data from the lower Nakdong River from 1993 to 2016, we examined the temporal changes at two scales-episodic (from weekly to monthly) and long-term (yearly)-and screened the potential environmental drivers. Phytoplankton community component patterns were modeled with the drivers as covariates, using multivariate autoregressive state-space (MARSS) models, to assess their response to environmental drivers and biotic interactions. We assumed that compared to taxonomic classification, functional classification would obtain a better identification of community response to temporal variability. Over 24 years, the succession patterns of the dominant taxonomic and functional groups decreased in diversity, with the greatest decreases in biomass of Bacillariophyceae and group D (mainly the diatom Stephanodiscus hantzschii), and coincided with the introduction of group H1 (dinitrogen-fixing nostocaleans). The potential drivers for these changes were precipitation, water level, and total nitrogen (TN) for taxonomic groups and TN, total phosphorus, and euphotic zone depth for functional groups. The results of the MARSS model and temporal trends for each driver indicated that the increases in the water level and light availability were mostly related with the taxonomic and functional groups, respectively. The model for functional groups proposed a total of 24 significant inter-group relationships, where five relationships supported the succession patterns of dominant groups in the Nakdong River. Combined with the effects of increased light availability, a positive relationship between groups H1 and M (mainly Cyanobacteria and Microcystis aeruginosa) appears to induce cyanobacterial bloom development over a long period. These results can be fundamental information for river system management concerning the resulting cascading effects of changes in environmental drivers and inter-group relationships on the phytoplankton community composition.

An improved methodology for quantifying causality in complex ecological systems

This paper provides a statistical methodology for quantifying causality in complex dynamical systems, based on analysis of multidimensional time series data of the state variables. The methodology integrates Granger's causality analysis based on the log-likelihood function expansion (Partial pair-wise causality), and Akaike's power contribution approach over the whole frequency domain (Total causality). The proposed methodology addresses a major drawback of existing methodologies namely, their inability to use time series observation of state variables to quantify causality in complex systems. We first perform a simulation study to verify the efficacy of the methodology using data generated by several multivariate autoregressive processes, and its sensitivity to data sample size. We demonstrate application of the methodology to real data by deriving inter-species relationships that define key food web drivers of the Barents Sea ecosystem. Our results show that the proposed methodology is a useful tool in early stage causality analysis of complex feedback systems.

Partitioning the Relative Importance of Phylogeny and Environmental Conditions on Phytoplankton Fatty Acids

Essential fatty acids (EFA), which are primarily generated by phytoplankton, limit growth and reproduction in diverse heterotrophs. The biochemical composition of phytoplankton is well-known to be governed both by phylogeny and environmental conditions. Nutrients, light, salinity, and temperature all affect both phytoplankton growth and fatty acid composition. However, the relative importance of taxonomy and environment on algal fatty acid content has yet to be comparatively quantified, thus inhibiting predictions of changes to phytoplankton food quality in response to global environmental change. We compiled 1145 published marine and freshwater phytoplankton fatty acid profiles, consisting of 208 species from six major taxonomic groups, cultured in a wide range of environmental conditions, and used a multivariate distance-based linear model to quantify the total variation explained by each variable. Our results show that taxonomic group accounts for 3-4 times more variation in phytoplankton fatty acids than the most important growth condition variables. The results underscore that environmental conditions clearly affect phytoplankton fatty acid profiles, but also show that conditions account for relatively low variation compared to phylogeny. This suggests that the underlying mechanism determining basal food quality in aquatic habitats is primarily phytoplankton community composition, and allows for prediction of environmental-scale EFA dynamics based on phytoplankton community data. We used the compiled dataset to calculate seasonal dynamics of long-chain EFA (LCEFA; ≥C₂₀ ɷ-3 and ɷ-6 polyunsaturated fatty acid) concentrations and ɷ-3:ɷ-6 EFA ratios in Lake Washington using a multi-decadal phytoplankton community time series. These analyses quantify temporal dynamics of algal-derived LCEFA and food quality in a freshwater ecosystem that has undergone large community changes as a result of shifting resource management practices, highlighting diatoms, cryptophytes and dinoflagellates as key sources of LCEFA. Moreover, the analyses indicate that future shifts towards cyanobacteria-dominated communities will result in lower LCEFA content in aquatic ecosystems.