Biodiversity in changing environments: An external-driver internal-topology framework to guide intervention

Accompanying the climate crisis is the more enigmatic biodiversity crisis. Rapid reorganization of biodiversity due to global environmental change has defied prediction and tested the basic tenets of conservation and restoration. Conceptual and practical innovation is needed to support decision making in the face of these unprecedented shifts. Critical questions include: How can we generalize biodiversity change at the community level? When are systems able to reorganize and maintain integrity, and when does abiotic change result in collapse or restructuring? How does this understanding provide a template to guide when and how to intervene in conservation and restoration? To this end, we frame changes in community organization as the modulation of external abiotic drivers on the internal topology of species interactions, using plant-plant interactions in terrestrial communities as a starting point. We then explore how this framing can help translate available data on species abundance and trait distributions to corresponding decisions in management. Given the expectation that community response and reorganization are highly complex, the external-driver internal-topology (EDIT) framework offers a way to capture general patterns of biodiversity that can help guide resilience and adaptation in changing environments.

Mimicking functional elements of the natural flow regime promotes native fish recovery in a regulated river

Streamflow regimes that maintain vital functions and processes of aquatic ecosystems are critical to sustaining ecosystem health. In rivers with altered flow regimes, restoring components of the natural flow regime is predicted to conserve freshwater biodiversity by supporting ecological functions and geomorphological processes to which native communities are adapted. However, the effectiveness of environmental flow restoration is poorly understood because of inadequate monitoring and uncertainty in ecological responses to managed changes in specific, quantifiable aspects of the annual streamflow regime. Here, we used time series models to analyze 25 years of fish assemblage data collected before and after environmental flow implementation in a dammed river in California, USA. We examined the response of the fish community to changes in individual components of the flow regime known to support ecosystem functions. We found that as functional flow components shifted toward their predicted natural range, the quasi-extinction risk (likelihood of population declines of >80%) decreased for the native fish assemblage. Following environmental flow implementation, observed changes toward natural ranges of dry season duration, fall pulse flow magnitude, and wet season timing each reduced quasi-extinction risk by at least 40% for the native assemblage. However, functional flow components that shifted away from their predicted natural range, including lower spring recession flows and higher dry season baseflow, resulted in greater quasi-extinction risk for native species. In contrast, non-native species decreased in abundance when flow components shifted toward predicted natural ranges and increased when components shifted away from their natural range. Although most functional flow components remained outside of their natural range following environmental flow implementation, our results indicate that even moderate shifts toward a natural flow regime can benefit native and suppress non-native fish species. Overall, this study provides the most compelling evidence to date of the effectiveness of functional environmental flows in supporting native fish recovery in a highly regulated river.

Different patterns of coupled predator-prey dynamics when the same species interact in different locations

Individual predator and prey species exhibit coupled population dynamics in simple laboratory systems and simple natural communities. It is unclear how often such pairwise coupling occurs in more complex communities, in which an individual predator species might feed on several prey species and an individual prey species might be attacked by several predators. To examine this problem, we applied multivariate autoregressive state-space (MARSS) models to 5-year time-series of monthly surveys of a predatory fish, the eastern mosquitofish (Gambusia holbrooki), and its littoral zone prey species, the least killifish (Heterandria formosa), in three locations in north Florida. The MARSS models were consistent with coupled predator-prey dynamics at two of the three locations. In one of these two locations, the estimated densities of the two species displayed classic predator-prey oscillations. In the third location, there was a positive effect of killifish density on mosquitofish density but no detectable effect of mosquitofish density on killifish density. In all three locations, increased submergent vegetation cover was associated with increased prey density but not increased predator density. Eigenvalues analyses for the joint predator-prey dynamics indicated that one of the cyclic locations had more stable dynamics than the other locations. The three different patterns demonstrate that the dynamics of a pairwise predator-prey interaction emerge not only from the characteristics of the prey and the predator, but also those of the habitat and trophic web in which the predator and prey are embedded.

Understanding diversity-synchrony-stability relationships in multitrophic communities

Understanding how species loss impacts ecosystem stability is critical given contemporary declines in global biodiversity. Despite decades of research on biodiversity-stability relationships, most studies are performed within a trophic level, overlooking the multitrophic complexity structuring natural communities. Here, in a global analysis of diversity-synchrony-stability (DSS) studies (n = 420), we found that 74% were monotrophic and biased towards terrestrial plant communities, with 91% describing stabilizing effects of asynchrony. Multitrophic studies (26%) were representative of all biomes and showed that synchrony had mixed effects on stability. To explore potential mechanisms, we applied a multitrophic framework adapted from DSS theory to investigate DSS relationships in algae-herbivore assemblages across five long-term tropical and temperate marine system datasets. Both algal and herbivore species diversity reduced within-group synchrony in both systems but had different interactive effects on species synchrony between systems. Herbivore synchrony was positively and negatively influenced by algal diversity in tropical versus temperate systems, respectively, and algal synchrony was positively influenced by herbivore diversity in temperate systems. While herbivore synchrony reduced multitrophic stability in both systems, algal synchrony only reduced stability in tropical systems. These results highlight the complexity of DSS relationships at the multitrophic level and emphasize why more multitrophic assessments are needed to better understand how biodiversity influences community stability in nature.

Effects of protection and temperature variation on temporal stability in a marine reserve network

Understanding the drivers of ecosystem stability has been a key focus of modern ecology as the impacts of the Anthropocene become more prevalent and extreme. Marine protected areas (MPAs) are tools used globally to promote biodiversity and mediate anthropogenic impacts. However, assessing the stability of natural ecosystems and responses to management actions is inherently challenging due to the complex dynamics of communities with many interdependent taxa. Using a 12-year time series of subtidal community structure in an MPA network in the Channel Islands (United States), we estimated species interaction strength (competition and predation), prey species synchrony, and temporal stability in trophic networks, as well as temporal variation in sea surface temperature to explore the causal drivers of temporal stability at community and metacommunity scales. At the community scale, only trophic networks in MPAs at Santa Rosa Island showed greater temporal stability than reference sites, likely driven by reduced prey synchrony. Across islands, competition was sometimes greater and predation always greater in MPAs compared with reference sites. Increases in interaction strength resulted in lower temporal stability of trophic networks. Although MPAs reduced prey synchrony at the metacommunity scale, reductions were insufficient to stabilize trophic networks. In contrast, temporal variation in sea surface temperature had strong positive direct effects on stability at the regional scale and indirect effects at the local scale through reductions in species interaction strength. Although MPAs can be effective management strategies for protecting certain species or locations, our findings for this MPA network suggest that temperature variation has a stronger influence on metacommunity temporal stability by mediating species interactions and promoting a mosaic of spatiotemporal variation in community structure of trophic networks. By capturing the full spectrum of environmental variation in network planning, MPAs will have the greatest capacity to promote ecosystem stability in response to climate change.

Density regulation amplifies environmentally induced population fluctuations

Background

Density-dependent regulation is ubiquitous in population dynamics, and its potential interaction with environmental stochasticity complicates the characterization of the random component of population dynamics. Yet, this issue has not received attention commensurate with its relevance for descriptive and predictive modeling of population dynamics. Here we use a Bayesian modeling approach to investigate the contribution of density regulation to population variability in stochastic environments.

Methods

We analytically derive a formula linking the stationary variance of population abundance/density under Gompertz regulation in a stochastic environment with constant variance to the environmental variance and the strength of density feedback, to investigate whether and how density regulation affects the stationary variance. We examine through simulations whether the relationship between stationary variance and density regulation inferred analytically under the Gompertz model carries over to the Ricker model, widely used in population dynamics modeling.

Results

The analytical decomposition of the stationary variance under stochastic Gompertz dynamics implies higher variability for strongly regulated populations. Simulation results demonstrate that the pattern of increasing population variability with increasing density feedback found under the Gompertz model holds for the Ricker model as well, and is expected to be a general phenomenon with stochastic population models. We also analytically established and empirically validated that the square of the autoregressive parameter of the Gompertz model in AR(1) form represents the proportion of stationary variance due to density dependence.

Discussion

Our results suggest that neither environmental stochasticity nor density regulation can alone explain the patterns of population variability in stochastic environments, as these two components of temporal variation interact, with a tendency for density regulation to amplify the magnitude of environmentally induced population fluctuations. This finding has far-reaching implications for population viability. It implies that intense intra-specific resource competition increases the risk of environment-driven population collapse at high density, making opportune harvesting a sensible practice for improving the resistance of managed populations such as fish stocks to environmental perturbations. The separation of density-dependent and density-independent processes will help improve population dynamics modeling, while providing a basis for evaluating the relative importance of these two categories of processes that remains a topic of long-standing controversy among ecologists.

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.

Four decades of climatic fluctuations and fish recruitment stability across a marine-freshwater gradient

Investigating the effects of climatic variability on biological diversity, productivity, and stability is key to understanding possible futures for ecosystems under accelerating climate change. A critical question for estuarine ecosystems is, how does climatic variability influence juvenile recruitment of different fish species and life histories that use estuaries as nurseries? Here we examined spatiotemporal abundance trends and environmental responses of 18 fish species that frequently spend the juvenile stage rearing in the San Francisco Estuary, CA, USA. First, we constructed multivariate autoregressive state-space models using age-0 fish abundance, freshwater flow (flow), and sea surface temperature data (SST) collected over four decades. Next, we calculated coefficients of variation (CV) to assess portfolio effects (1) within and among species, life histories (anadromous, marine opportunist, or estuarine dependent), and the whole community; and (2) within and among regions of the estuary. We found that species abundances varied over space and time (increasing, decreasing, or dynamically stable); and in 83% of cases, in response to environmental conditions (wet/dry, cool/warm periods). Anadromous species responded strongly to flow in the upper estuary, marine opportunist species responded to flow and/or SST in the lower estuary, and estuarine dependent species had diverse responses across the estuary. Overall, the whole community when considered across the entire estuary had the lowest CV, and life histories and species provided strong biological insurance to the portfolio (2.4- to 3.5-fold increases in stability, respectively). Spatial insurance also increased stability, although to a lesser extent (up to 1.6-fold increases). Our study advances the notion that fish recruitment stability in estuaries is controlled by biocomplexity-life history diversity and spatiotemporal variation in the environment. However, intensified drought and marine heatwaves may increase the risk of multiple consecutive recruitment failures by synchronizing species dynamics and trajectories via Moran effects, potentially diminishing estuarine nursery function.

Understanding the combined effects of multiple stressors: A new perspective on a longstanding challenge

Wildlife populations and their habitats are exposed to an expanding diversity and intensity of stressors caused by human activities, within the broader context of natural processes and increasing pressure from climate change. Estimating how these multiple stressors affect individuals, populations, and ecosystems is thus of growing importance. However, their combined effects often cannot be predicted reliably from the individual effects of each stressor, and we lack the mechanistic understanding and analytical tools to predict their joint outcomes. We review the science of multiple stressors and present a conceptual framework that captures and reconciles the variety of existing approaches for assessing combined effects. Specifically, we show that all approaches lie along a spectrum, reflecting increasing assumptions about the mechanisms that regulate the action of single stressors and their combined effects. An emphasis on mechanisms improves analytical precision and predictive power but could introduce bias if the underlying assumptions are incorrect. A purely empirical approach has less risk of bias but requires adequate data on the effects of the full range of anticipated combinations of stressor types and magnitudes. We illustrate how this spectrum can be formalised into specific analytical methods, using an example of North Atlantic right whales feeding on limited prey resources while simultaneously being affected by entanglement in fishing gear. In practice, case-specific management needs and data availability will guide the exploration of the stressor combinations of interest and the selection of a suitable trade-off between precision and bias. We argue that the primary goal for adaptive management should be to identify the most practical and effective ways to remove or reduce specific combinations of stressors, bringing the risk of adverse impacts on populations and ecosystems below acceptable thresholds.

A keystone gene underlies the persistence of an experimental food web

Genes encode information that determines an organism's fitness. Yet we know little about whether genes of one species influence the persistence of interacting species in an ecological community. Here, we experimentally tested the effect of three plant defense genes on the persistence of an insect food web and found that a single allele at a single gene promoted coexistence by increasing plant growth rate, which in turn increased the intrinsic growth rates of species across multiple trophic levels. Our discovery of a "keystone gene" illustrates the need to bridge between biological scales, from genes to ecosystems, to understand community persistence.

Biological traits can mediate species-specific, quasi-extinction risks of macroinvertebrates in streams experiencing frequent extreme floods

Most research on the ecological responses to extreme floods examines impacts at short time scales, whereas long-term datasets combining hydrological and biological information remain rare. Using such data, we applied time-series analysis to investigate simultaneous effects of a biotic factor (density dependence), an abiotic factor (extreme floods), and spatial synchrony in the population dynamics of three riverine insects. Spatial synchronization of population dynamics by extreme floods varied among species. These different responses to extreme floods could be explained by species-specific biological traits. Moreover, density dependence influenced the population dynamics under the context of extreme floods. Accordingly, quasi-extinction risks were highest for species that were simultaneously influenced by biotic and abiotic factors. An understanding of ecological responses to increasing hydrological extremes may be enhanced by recognizing long-term, climatic non-stationarity.

Refocusing multiple stressor research around the targets and scales of ecological impacts

Ecological communities face a variety of environmental and anthropogenic stressors acting simultaneously. Stressor impacts can combine additively or can interact, causing synergistic or antagonistic effects. Our knowledge of when and how interactions arise is limited, as most models and experiments only consider the effect of a small number of non-interacting stressors at one or few scales of ecological organization. This is concerning because it could lead to significant underestimations or overestimations of threats to biodiversity. Furthermore, stressors have been largely classified by their source rather than by the mechanisms and ecological scales at which they act (the target). Here, we argue, first, that a more nuanced classification of stressors by target and ecological scale can generate valuable new insights and hypotheses about stressor interactions. Second, that the predictability of multiple stressor effects, and consistent patterns in their impacts, can be evaluated by examining the distribution of stressor effects across targets and ecological scales. Third, that a variety of existing mechanistic and statistical modelling tools can play an important role in our framework and advance multiple stressor research.

Drought effects on invertebrate metapopulation dynamics and quasi-extinction risk in an intermittent river network

Ecological communities can remain stable in the face of disturbance if their constituent species have different resistance and resilience strategies. In turn, local stability scales up regionally if heterogeneous landscapes maintain spatial asynchrony across discrete populations-but not if large-scale stressors synchronize environmental conditions and biological responses. Here, we hypothesized that droughts could drastically decrease the stability of invertebrate metapopulations both by filtering out poorly adapted species locally, and by synchronizing their dynamics across a river network. We tested this hypothesis via multivariate autoregressive state-space (MARSS) models on spatially replicated, long-term data describing aquatic invertebrate communities and hydrological conditions in a set of temperate, lowland streams subject to seasonal and supraseasonal drying events. This quantitative approach allowed us to assess the influence of local (flow magnitude) and network-scale (hydrological connectivity) drivers on invertebrate long-term trajectories, and to simulate near-future responses to a range of drought scenarios. We found that fluctuations in species abundances were heterogeneous across communities and driven by a combination of hydrological and stochastic drivers. Among metapopulations, increasing extent of dry reaches reduced the abundance of functional groups with low resistance or resilience capacities (i.e. low ability to persist in situ or recolonize from elsewhere, respectively). Our simulations revealed that metapopulation quasi-extinction risk for taxa vulnerable to drought increased exponentially as flowing habitats contracted within the river network, whereas the risk for taxa with resistance and resilience traits remained stable. Our results suggest that drought can be a synchronizing agent in riverscapes, potentially leading to regional quasi-extinction of species with lower resistance and resilience abilities. Better recognition of drought-driven synchronization may increase realism in species extinction forecasts as hydroclimatic extremes continue to intensify worldwide.

The application of metacommunity theory to the management of riverine ecosystems

River managers strive to use the best available science to sustain biodiversity and ecosystem function. To achieve this goal requires consideration of processes at different scales. Metacommunity theory describes how multiple species from different communities potentially interact with local-scale environmental drivers to influence population dynamics and community structure. However, this body of knowledge has only rarely been used to inform management practices for river ecosystems. In this paper, we present a conceptual model outlining how the metacommunity processes of local niche sorting and dispersal can influence the outcomes of management interventions and provide a series of specific recommendations for applying these ideas as well as research needs. In all cases, we identify situations where traditional approaches to riverine management could be enhanced by incorporating an understanding of metacommunity dynamics. A common theme is developing guidelines for assessing the metacommunity context of a site or region, evaluating how that context may affect the desired outcome, and incorporating that understanding into the planning process and methods used. To maximize the effectiveness of management activities, scientists and resource managers should update the toolbox of approaches to riverine management to reflect theoretical advances in metacommunity ecology.

When and Where We Can Expect to See Early Warning Signals in Multispecies Systems Approaching Tipping Points: Insights from Theory

AbstractEarly warning signals (EWSs) have the potential to predict tipping points where catastrophic changes occur in ecological systems. However, EWSs are plagued by false negatives, leading to undetected catastrophes. One reason may be because EWSs do not occur equally for all species in a system, so whether and how strongly EWSs are detected depends on which species is being observed. Here, we illustrate how the strength of EWSs is determined by each species' relationship to properties of the noise, the system's response to that noise, and the occurrence of critical slowing down (the dynamical phenomenon that gives rise to EWSs). Using these relationships, we present general rules for maximizing EWS detection in ecological communities. We find that for two-species competitive and mutualistic systems, one should generally monitor the species experiencing smaller intraspecific effects to maximize EWS performance, while in consumer-resource systems, one should monitor the species imposing the smaller interspecific effects. These guidelines appear to hold for at least some larger communities as well. We close by extending the theoretical basis for our rules to systems with any number of species and more complex forms of noise. Our findings provide important guidance on how to monitor systems for EWSs to maximize detection of tipping points.

Warming-driven shifts in ecological control of fish communities in a large northern Chinese lake over 66 years

Warming, land-use change, and habitat loss are three major threats to aquatic biodiversity worldwide under the influences of anthropogenic disturbances. Positive feedback between warming and bottom-up regulation may cause irreversible ecological regime shifts. Threshold dynamics of interspecific interactions have been rarely studied in freshwater fish communities using threshold community models. Here we use 66 years (1950-2015) of data to link four ecological regime shifts of 9-species fish communities to climatic and land use changes in Lake Hulun, the largest freshwater lake of Northern China. Overfishing caused the collapse of piscivorous fish populations and an ecological regime shift of Lake Hulun in the late 1950s. The first recorded algal bloom of Lake Hulun took place in 1986, with accelerated warming and rapid increases in livestock grazing. The dominance of planktivorous minnow populations reduced fish biodiversity in a nonlinear, threshold manner when annual mean ambient temperature was >0.12 °C. Multivariate environmental vector regression demonstrated that warming, eutrophication, and water-storage reduction (i.e., habitat loss) were related to three ecological regime shifts of Lake Hulun from 1960 to 2015. Multivariate autoregressive models (MAR) did not detect predation by piscivorous fish in Lake Hulun after 1960. Threshold MAR models indicated that dominant minnow populations and other prey fish populations switched from top-down to bottom-up control during the 1980s. Sustained positive feedback between warming, the dominance of planktivorous fish populations, and bottom-up regulation caused predator-prey role reversal, and probably resulted in three regime shifts of Lake Hulun over 56 years. This study provides a comprehensive analysis of ecological regime shifts in Hulun Lake fish communities, and has potential implications for fish species living in similar environments that are subject to global warming, land-use changes, and overfishing.

Developmental Change in Predators Drives Different Community Configurations

AbstractTheoreticians who first observed alternative stable states in simple ecological models warned of grave implications for unexpected and irreversible collapses of natural systems (i.e., regime shifts). Recent ecosystem-level shifts engendering considerable economic losses have validated this concern, positioning bistability at the vanguard of coupled human-environment systems management. While the perturbations that induce regime shifts are known, the ecological forces that uphold alternative stable states are often unresolved or complex and system specific. Thus, the search continues for general mechanisms that can produce alternative stable states under realistic conditions. Integrating model predictions with long-term zooplankton community experiments, we show that the core feature of ontogenetic development, food-dependent maturation, enables a single community to reach different configurations within the same constant environment. In one configuration, predators regulate prey to foster coexistence, while in the other, prey counterintuitively exclude their predators via maturation-limiting competition. The concordance of these findings with the unique outcome and underlying mechanism of a general model provides empirical evidence that developmental change, a fundamental property of life, can support bistability of natural systems.

Changes in the analysis of temporal community dynamics data: a 29-year literature review

BACKGROUND

Understanding how biological communities change over time is of increasing importance as Earth moves into the Anthropocene. A wide variety of methods are used for multivariate community analysis and are variously applied to research that aims to characterise temporal dynamics in community composition. Understanding these methods and how they are applied is useful for determining best practice in community ecology.

METHODOLOGY

We reviewed the ecological literature from 1990 to 2018 that used multivariate methods to address questions of temporal community dynamics. For each paper that fulfilled our search criteria, we recorded the types of multivariate analysis used to characterise temporal community dynamics in addition to the research aim, habitat type, location, taxon and the experimental design.

RESULTS

Most studies had relatively few temporal replicates; the median number was seven time points. Nearly 70% of studies applied more than one analysis method; descriptive methods such as bar graphs and ordination were the most commonly applied methods. Surprisingly, the types of analyses used were only related to the number of temporal replicates, but not to research aim or any other aspects of experimental design such as taxon, or habitat or year of study.

CONCLUSIONS

This review reveals that most studies interested in understanding community dynamics use relatively short time series meaning that several, more sophisticated, temporal analyses are not widely applicable. However, newer methods using multivariate dissimilarities are growing in popularity and many can be applied to time series of any length.

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.

Model-based Comparisons of the Abundance Dynamics of Bacterial Communities in Two Lakes

Lake Lanier (Georgia, USA) is home to more than 11,000 microbial Operational Taxonomic Units (OTUs), many of which exhibit clear annual abundance patterns. To assess the dynamics of this microbial community, we collected time series data of 16S and 18S rRNA gene sequences, recovered from 29 planktonic shotgun metagenomic datasets. Based on these data, we constructed a dynamic mathematical model of bacterial interactions in the lake and used it to analyze changes in the abundances of OTUs. The model accounts for interactions among 14 sub-communities (SCs), which are composed of OTUs blooming at the same time of the year, and three environmental factors. It captures the seasonal variations in abundances of the SCs quite well. Simulation results suggest that changes in water temperature affect the various SCs differentially and that the timing of perturbations is critical. We compared the model results with published results from Lake Mendota (Wisconsin, USA). These comparative analyses between lakes in two very different geographical locations revealed substantially more cooperation and less competition among species in the warmer Lake Lanier than in Lake Mendota.

Informing management decisions for ecological networks, using dynamic models calibrated to noisy time-series data

Well-intentioned environmental management can backfire, causing unforeseen damage. To avoid this, managers and ecologists seek accurate predictions of the ecosystem-wide impacts of interventions, given small and imprecise datasets, which is an incredibly difficult task. We generated and analysed thousands of ecosystem population time series to investigate whether fitted models can aid decision-makers to select interventions. Using these time-series data (sparse and noisy datasets drawn from deterministic Lotka-Volterra systems with two to nine species, of known network structure), dynamic model forecasts of whether a species' future population will be positively or negatively affected by rapid eradication of another species were correct > 70% of the time. Although 70% correct classifications is only slightly better than an uninformative prediction (50%), this classification accuracy can be feasibly improved by increasing monitoring accuracy and frequency. Our findings suggest that models may not need to produce well-constrained predictions before they can inform decisions that improve environmental outcomes.

Soil microbial community responses to climate extremes: resistance, resilience and transitions to alternative states

A major challenge for advancing our understanding of the functional role of soil microbial communities is to link changes in their structure and function under climate change. To address this challenge requires new understanding of the mechanisms that underlie the capacity of soil microbial communities to resist and recover from climate extremes. Here, we synthesize emerging understanding of the intrinsic and extrinsic factors that influence the resistance and resilience of soil microbial communities to climate extremes, with a focus on drought, and identify drivers that might trigger abrupt changes to alternative states. We highlight research challenges and propose a path for advancing our understanding of the resistance and resilience of soil microbial communities to climate extremes, and of their vulnerability to transitions to alternative states, including the use of trait-based approaches. We identify a need for new approaches to quantify resistance and resilience of soil microbial communities, and to identify thresholds for transitions to alternative states. We show how high-resolution time series coupled with gradient designs will enable detecting response patterns to interacting drivers. Finally, to account for extrinsic factors, we suggest that future studies should use environmental gradients to track soil microbial community responses to climate extremes in space and time. This article is part of the theme issue 'Climate change and ecosystems: threats, opportunities and solutions'.

Environmental variables driving species and genus level changes in annual plankton biomass

Abiotic variables subject to global change are known to affect plankton biomasses, and these effects can be species-specific. Here, we investigate the environmental drivers of annual biomass using plankton data from the Gulf of Finland in the northern Baltic Sea, spanning years 1993-2016. We estimated annual biomass time-series of 31 nanoplankton and microplankton species and genera from day-level data, accounting for the average phenology and wind. We found wind effects on day-level biomass in 16 taxa. We subsequently used state-space models to connect the annual biomass changes with potential environmental drivers (temperature, salinity, stratification, ice cover and inorganic nutrients), simultaneously accounting for temporal trends. We found clear environmental effects influencing the annual biomasses of Dinobryon faculiferum, Eutreptiella spp., Protoperidinium bipes, Pseudopedinella spp., Snowella spp. and Thalassiosira baltica and indicative effects in 10 additional taxa. These effects mostly concerned temperature, salinity or stratification. Together, these 16 taxa represent two-thirds of the summer biomass in the sampled community. The inter-annual variability observed in salinity and temperature is relatively low compared to scenarios of predicted change in these variables. Therefore, the potential impacts of the presented effects on plankton biomasses are considerable.

Dam Removal Effects on Benthic Macroinvertebrate Dynamics: A New England Stream Case Study (Connecticut, USA)

Dam removal is an increasingly common stream restoration tool. Yet, removing dams from small streams also represents a major disturbance to rivers that can have varied impacts on environmental conditions and aquatic biota. We examined the effects of dam removal on the structure, function, and composition of benthic macroinvertebrate (BMI) communities in a temperate New England stream. We examined the effects of dam removal over the dam removal time-series using linear mixed effects models, autoregressive models, non-metric multidimensional scaling, and indicator and similarity analyses. The results indicated that the dam removal stimulated major shifts in BMI community structure and composition above and below the dam, and that the BMI communities are becoming more similar over time. The mixed model analysis revealed that BMI functional groups and diversity were significantly influenced by sample site and several BMI groups also experienced significant interactions between site and dam stage (P < 0.05), while the multivariate analyses revealed that community structure continues to differ among sites, even three years after dam removal. Our findings indicate that stream restoration through dam removal can have site-specific influences on BMI communities, that interactions among BMI taxa are important determinants of the post-dam removal community, and that the post-dam-removal BMI community continues to be in a state of reorganization.

Predator-prey feedback in a gyrfalcon-ptarmigan system?

Specialist predators with oscillating dynamics are often strongly affected by the population dynamics of their prey, yet they are not always the cause of prey cycling. Only those that exert strong (delayed) regulation of their prey can be. Inferring predator-prey coupling from time series therefore requires contrasting models with top-down versus bottom-up predator-prey dynamics. We study here the joint dynamics of population densities of the Icelandic gyrfalcon Falco rusticolus, and its prey, the rock ptarmigan Lagopus muta. The dynamics of both species are likely not only linked to each other but also to stochastic weather variables acting as confounding factors. We infer the degree of coupling between populations, as well as forcing by abiotic variables, using multivariate autoregressive models MAR(p), with p = 1 and 2 time lags. MAR(2) models, allowing for species to cycle independently from each other, further suggest alternative scenarios where a cyclic prey influences its predator but not the other way around (i.e., bottom-up scenarios). The classical MAR(1) model predicts that the time series exhibit predator-prey feedback (i.e., reciprocal dynamic influence between prey and predator), and that weather effects are weak and only affecting the gyrfalcon population. Bottom-up MAR(2) models produced a better fit but less realistic cross-correlation patterns. Simulations of MAR(1) and MAR(2) models further demonstrate that the top-down MAR(1) models are more likely to be misidentified as bottom-up dynamics than vice versa. We therefore conclude that predator-prey feedback in the gyrfalcon-ptarmigan system is likely the main cause of observed oscillations, though bottom-up dynamics cannot yet be excluded with certainty. Overall, we showed how to make more out of ecological time series by using simulations to gauge the quality of model identification, and paved the way for more mechanistic modeling of this system by narrowing the set of important biotic and abiotic drivers.

Detrimental effects of a novel flow regime on the functional trajectory of an aquatic invertebrate metacommunity

Novel flow regimes resulting from dam operations and overallocation of freshwater resources are an emerging consequence of global change. Yet, anticipating how freshwater biodiversity will respond to surging flow regime alteration requires overcoming two challenges in environmental flow science: shifting from local to riverscape-level understanding of biodiversity dynamics, and from static to time-varying characterizations of the flow regime. Here, we used time-series methods (wavelets and multivariate autoregressive models) to quantify flow-regime alteration and to link time-varying flow regimes to the dynamics of multiple local communities potentially connected by dispersal (i.e., a metacommunity). We studied the Chattahoochee River below Buford dam (Georgia, U.S.A.), and asked how flow regime alteration by a large hydropower dam may control the long-term functional trajectory of the downstream invertebrate metacommunity. We found that seasonal variation in hydropeaking synchronized temporal fluctuations in trait abundance among the flow-altered sites. Three biological trait states describing adaptation to fast flows benefitted from flow management for hydropower, but did not compensate for declines in 16 "loser" traits. Accordingly, metacommunity-wide functional diversity responded negatively to hydropeaking intensity, and stochastic simulations showed that the risk of functional diversity collapse within the next 4 years would decrease by 17% if hydropeaking was ameliorated, or by 9% if it was applied every other season. Finally, an analysis of 97 reference and 23 dam-affected river sites across the U.S. Southeast suggested that flow variation at extraneous, human-relevant scales (12-hr, 24-hr, 1-week) is relatively common in rivers affected by hydropower dams. This study advances the notion that novel flow regimes are widespread, and simplify the functional structure of riverine communities by filtering out taxa with nonadaptive traits and by spatially synchronizing their dynamics. This is relevant in the light of ongoing and future hydrologic alteration due to climate non-stationarity and the new wave of dams planned globally.

Density-dependent and density-independent drivers of population change in Barton Springs salamanders

Understanding population change is essential for conservation of imperiled species, such as amphibians. Worldwide amphibian declines have provided an impetus for investigating their population dynamics, which can involve both extrinsic (density-independent) and intrinsic (density-dependent) drivers acting differentially across multiple life stages or age classes. In this study, we examined the population dynamics of the endangered Barton Springs Salamander (Eurycea sosorum) using data from a long-term monitoring program. We were interested in understanding both the potential environmental drivers (density-independent factors) and demographic factors (interactions among size classes, negative density dependence) to better inform conservation and management activities. We used data from three different monitoring regimes and multivariate autoregressive state-space models to quantify environmental effects (seasonality, discharge, algae, and sediment cover), intraspecific interactions among three size classes, and intra-class density dependence. Results from our primary data set revealed similar patterns among sites and size classes and were corroborated by our out-of-sample data. Cross-correlation analysis showed juvenile abundance was most strongly correlated with a 9-month lag in aquifer discharge, which we suspect is related to inputs of organic carbon into the aquifer. However, sedimentation limited juvenile abundance at the surface, emphasizing the importance of continued sediment management. Recruitment from juveniles to the sub-adult size class was evident, but negative density-dependent feedback ultimately regulated each size class. Negative density dependence may be an encouraging sign for the conservation of E. sosorum because populations that can reach carrying capacity are less likely to go extinct compared to unregulated populations far below their carrying capacity. However, periodic population declines coupled with apparent migration into the aquifer complicate assessments of species status. Although both density-dependent and density-independent drivers of population change are not always apparent in time series of animal populations, both have important implications for conservation and management of E. sosorum.

How are species interactions structured in species-rich communities? A new method for analysing time-series data

Estimation of intra- and interspecific interactions from time-series on species-rich communities is challenging due to the high number of potentially interacting species pairs. The previously proposed sparse interactions model overcomes this challenge by assuming that most species pairs do not interact. We propose an alternative model that does not assume that any of the interactions are necessarily zero, but summarizes the influences of individual species by a small number of community-level drivers. The community-level drivers are defined as linear combinations of species abundances, and they may thus represent e.g. the total abundance of all species or the relative proportions of different functional groups. We show with simulated and real data how our approach can be used to compare different hypotheses on community structure. In an empirical example using aquatic microorganisms, the community-level drivers model clearly outperformed the sparse interactions model in predicting independent validation data.

Among-site variability in the stochastic dynamics of East African coral reefs

Coral reefs are dynamic systems whose composition is highly influenced by unpredictable biotic and abiotic factors. Understanding the spatial scale at which long-term predictions of reef composition can be made will be crucial for guiding conservation efforts. Using a 22-year time series of benthic composition data from 20 reefs on the Kenyan and Tanzanian coast, we developed Bayesian vector autoregressive state-space models for reef dynamics, incorporating among-site variability, and quantified their long-term behaviour. We estimated that if there were no among-site variability, the total long-term variability would be approximately one-third of its current value. Thus, our results showed that among-site variability contributes more to long-term variability in reef composition than does temporal variability. Individual sites were more predictable than previously thought, and predictions based on current snapshots are informative about long-term properties. Our approach allowed us to identify a subset of possible climate refugia sites with high conservation value, where the long-term probability of coral cover ≤0.1 (as a proportion of benthic cover of hard substrate) was very low. Analytical results show that this probability is most strongly influenced by among-site variability and by interactions among benthic components within sites. These findings suggest that conservation initiatives might be successful at the site scale as well as the regional scale.

Population assessment using multivariate time-series analysis: A case study of rockfishes in Puget Sound

Estimating a population's growth rate and year-to-year variance is a key component of population viability analysis (PVA). However, standard PVA methods require time series of counts obtained using consistent survey methods over many years. In addition, it can be difficult to separate observation and process variance, which is critical for PVA. Time-series analysis performed with multivariate autoregressive state-space (MARSS) models is a flexible statistical framework that allows one to address many of these limitations. MARSS models allow one to combine surveys with different gears and across different sites for estimation of PVA parameters, and to implement replication, which reduces the variance-separation problem and maximizes informational input for mean trend estimation. Even data that are fragmented with unknown error levels can be accommodated. We present a practical case study that illustrates MARSS analysis steps: data choice, model set-up, model selection, and parameter estimation. Our case study is an analysis of the long-term trends of rockfish in Puget Sound, Washington, based on citizen science scuba surveys, a fishery-independent trawl survey, and recreational fishery surveys affected by bag-limit reductions. The best-supported models indicated that the recreational and trawl surveys tracked different, temporally independent assemblages that declined at similar rates (an average of -3.8% to -3.9% per year). The scuba survey tracked a separate increasing and temporally independent assemblage (an average of 4.1% per year). Three rockfishes (bocaccio, canary, and yelloweye) are listed in Puget Sound under the US Endangered Species Act (ESA). These species are associated with deep water, which the recreational and trawl surveys sample better than the scuba survey. All three ESA-listed rockfishes declined as a proportion of recreational catch between the 1970s and 2010s, suggesting that they experienced similar or more severe reductions in abundance than the 3.8-3.9% per year declines that were estimated for rockfish populations sampled by the recreational and trawl surveys.

Effects of Dam Removal on Fish Community Interactions and Stability in the Eightmile River System, Connecticut, USA

New multivariate time-series methods have the potential to provide important insights into the effects of ecosystem restoration activities. To this end, we examined the temporal effects of dam removal on fish community interactions using multivariate autoregressive models to understand changes in fish community structure in the Eightmile River System, Connecticut, USA. We sampled fish for 6 years during the growing season; 1 year prior to, 2 years during, and for 3 years after a small dam removal event. The multivariate autoregressive analysis revealed that the site above the dam was the most reactive and least resilient sample site, followed in order by the below-dam and nearby reference site. Even 3 years after the dam removal event, the stream was still in a recovery stage that had failed to approximate the community structure of the reference site. This suggests that the reorganization of fish communities following dam removals, with the goal of ecological restoration, may take decades to centuries for the restored sites to approximate the community structure of nearby undisturbed sites. Results from this study also highlight the utility of multivariate autoregressive modeling for examining temporal interactions among species in response to adaptive management activities both in aquatic systems and elsewhere.