A guide to ecosystem models and their environmental applications

A century of statistical Ecology

As data and computing power have surged in recent decades, statistical modeling has become an important tool for understanding ecological patterns and processes. Statistical modeling in ecology faces two major challenges. First, ecological data may not conform to traditional methods, and second, professional ecologists often do not receive extensive statistical training. In response to these challenges, the journal Ecology has published many innovative statistical ecology papers that introduced novel modeling methods and provided accessible guides to statistical best practices. In this paper, we reflect on Ecology's history and its role in the emergence of the subdiscipline of statistical ecology, which we define as the study of ecological systems using mathematical equations, probability, and empirical data. We showcase 36 influential statistical ecology papers that have been published in Ecology over the last century and, in so doing, comment on the evolution of the field. As data and computing power continue to increase, we anticipate continued growth in statistical ecology to tackle complex analyses and an expanding role for Ecology to publish innovative and influential papers, advancing the discipline and guiding practicing ecologists.

Machine learning prediction on wetland succession and the impact of artificial structures from a decade of field data

The artificial structures can influence wetland topology and sediment properties, thereby shaping plant distribution and composition. Macrobenthos composition was correlated with plant cover. Previous studies on the impact of artificial structures on plant distribution are scarce in incorporating time-series data or extended field surveys. In this study, a machine-learning-based species distribution model with decade-long observation was analyzed to investigate the correlation between the shift in the distribution of B. planiculmis, artificial structure-induced elevation changes and the expansion of other plants, as well as their connection to soil properties and crab composition dynamics under plants in Gaomei Wetland. Long short-term memory model (LSTM) with Shapley additive explanations (SHAP) was employed for predicting the distribution of B. planiculmis and explaining feature importance. The results indicated that wetland topology was influenced by both artificial structures and plants. Areas initially colonized by B. planiculmis were replaced by other species. Soil properties showed significant differences among plant patches; however, principal component analysis (PCA) of sediment properties and niche similarity analysis showed that the niche of plants was overlapped. Crab composition was different under different plants. The presence probability of B. planiculmis near woody paths decreased according to LSTM and field survey data. SHAP analysis suggested that the distribution of other plants, historical distribution of B. planiculmis and sediment properties significantly contributed to the presence probability of B. planiculmis. A sharp decrease in SHAP values with increasing NDVI at suitable elevations, overlap in PCA of sediment properties and niche similarity indicated potential competition among plants. This decade-long time-series field survey revealed the joint effects of artificial structure and vegetation on the topology and soil properties dynamics. These changes influenced the plant distribution through potential plant competition. LSTM with SHAP provided valuable insights in the underlying the mechanisms of artificial structure effects on the plant zonation process.

Climate change impact on sub-tropical lakes - Lake Kinneret as a case study

Climate change is anticipated to alter lake ecosystems by affecting water quality, potentially resulting in loss of ecosystem services. Subtropical lakes have high temperatures to begin with and are expected to exhibit higher temperatures all year round which might affect the thermal structure and ecological processes in a different manner than lakes in temperate zones. In this study the ecosystem response of the sub-tropical Lake Kinneret to climate change was explored using lake ecosystem models. Projection reliability was increased by using a weather generator and ensemble modelling, confronting uncertainty of both climate projections and lake models. The study included running two 1D hydrodynamic-biogeochemical models over one thousand realizations of two gradual temperature increase scenarios that span over 49 years. Our predictions show that an increase in air temperature would have subtle effects on stratification properties but may result in considerable changes to biogeochemical processes. Water temperature rise would cause a reduction in dissolved oxygen. Both of these changes would produce elevated phosphate and lowered ammonium concentrations. In turn, these changes are predicted to modify the phytoplankton community, expressed chiefly in increased cyanobacteria blooms at the expense of green phytoplankton and dinoflagellates; these changes may culminate in overall reduction of primary production. Identification of these trends would not be possible without the use of many realizations of climate scenarios. The use of ensemble modelling increased prediction reliability and highlighted elements of uncertainty. Though we use Lake Kinneret, the patterns identified most likely indicate processes that are expected in sub-tropical lakes in general.

Fire suppression makes wildfires more severe and accentuates impacts of climate change and fuel accumulation

Fire suppression is the primary management response to wildfires in many areas globally. By removing less-extreme wildfires, this approach ensures that remaining wildfires burn under more extreme conditions. Here, we term this the "suppression bias" and use a simulation model to highlight how this bias fundamentally impacts wildfire activity, independent of fuel accumulation and climate change. We illustrate how attempting to suppress all wildfires necessarily means that fires will burn with more severe and less diverse ecological impacts, with burned area increasing at faster rates than expected from fuel accumulation or climate change. Over a human lifespan, the modeled impacts of the suppression bias exceed those from fuel accumulation or climate change alone, suggesting that suppression may exert a significant and underappreciated influence on patterns of fire globally. Managing wildfires to safely burn under low and moderate conditions is thus a critical tool to address the growing wildfire crisis.

Unlocking ensemble ecosystem modelling for large and complex networks

The potential effects of conservation actions on threatened species can be predicted using ensemble ecosystem models by forecasting populations with and without intervention. These model ensembles commonly assume stable coexistence of species in the absence of available data. However, existing ensemble-generation methods become computationally inefficient as the size of the ecosystem network increases, preventing larger networks from being studied. We present a novel sequential Monte Carlo sampling approach for ensemble generation that is orders of magnitude faster than existing approaches. We demonstrate that the methods produce equivalent parameter inferences, model predictions, and tightly constrained parameter combinations using a novel sensitivity analysis method. For one case study, we demonstrate a speed-up from 108 days to 6 hours, while maintaining equivalent ensembles. Additionally, we demonstrate how to identify the parameter combinations that strongly drive feasibility and stability, drawing ecological insight from the ensembles. Now, for the first time, larger and more realistic networks can be practically simulated and analysed.

Meeting the Growing Need for Land-Water System Modelling to Assess Land Management Actions

Elevated contaminant levels and hydrological alterations resulting from land use are degrading aquatic ecosystems on a global scale. A range of land management actions may be used to reduce or prevent this degradation. To select among alternative management actions, decision makers require predictions of their effectiveness, their economic impacts, estimated uncertainty in the predictions, and estimated time lags between management actions and environmental responses. There are multiple methods for generating these predictions, but the most rigorous and transparent methods involve quantitative modelling. The challenge for modellers is two-fold. First, they must employ models that represent complex land-water systems, including the causal chains linking land use to contaminant loss and water use, catchment processes that alter contaminant loads and flow regimes, and ecological responses in aquatic environments. Second, they must ensure that these models meet the needs of endusers in terms of reliability, usefulness, feasibility and transparency. Integrated modelling using coupled models to represent the land-water system can meet both challenges and has advantages over alternative approaches. The need for integrated land-water system modelling is growing as the extent and intensity of human land use increases, and regulatory agencies seek more effective land management actions to counter the adverse effects. Here we present recommendations for modelling teams, to help them improve current practices and meet the growing need for land-water system models. The recommendations address several aspects of integrated modelling: (1) assembling modelling teams; (2) problem framing and conceptual modelling; (3) developing spatial frameworks; (4) integrating economic and biophysical models; (5) selecting and coupling models.

Ecosystem-level responses to multiple stressors using a time-dynamic food-web model: The case of a re-oligotrophicated coastal embayment (Saronikos Gulf, E Mediterranean)

Multiple stressors may combine in unexpected ways to alter the structure of ecological systems, however, our current ability to evaluate their ecological impact is limited due to the lack of information concerning historic trophic interactions and ecosystem dynamics. Saronikos Gulf is a heavily exploited embayment in the E Mediterranean that has undergone significant ecological alterations during the last 20 years including a shift from long-standing eutrophic to oligotrophic conditions in the mid-2000's. Here we used a historical Ecopath food-web model of Saronikos Gulf (1998-2000) and fitted the time-dynamic module Ecosim to biomass and catch time series for the period 2001-2020. We then projected the model forward in time from 2021 to 2050 under 8 scenarios to simulate ecosystem responses to the individual and combined effect of sea surface temperature increase, primary productivity shifts and fishing effort release. Incorporating trophic interactions, climate warming, fishing and primary production improved model fit, depicting that both fishing and the environment have historically influenced ecosystem dynamics. Retrospective simulations of the model captured historical biomass and catch trends of commercially important stocks and reproduced successfully the marked recovery of marine resources 10 years after re-oligotrophication. In future scenarios increasing temperature had a detrimental impact on most functional groups, increasing and decreasing productivity had a positive and negative effect on all respectively, while fishing reductions principally benefited top predators. Combined stressors produced synergistic or antagonistic effects depending on the direction and magnitude of change of each stressor in isolation while their overall impact seemed to be strongly mediated via food-web interactions. Such holistic approaches advance of our mechanistic understanding of ecosystems enabling us to develop more effective management strategies in the face of a rapidly changing marine environment.

Toward a network perspective in coastal ecosystem management

Environmental management in coastal ecosystems has been challenged by the complex cumulative effects that occur when many small issues result in large ecological shifts. Current environmental management of these spaces focuses on identifying and limiting problematic stressors via a series of assessment techniques. Whilst there is a strong desire among managers to consider complexity in ecological responses to cumulative effects, current approaches for assessing risk focus on breaking down the issues into multiple cause and effect relationships. However, uncertainty arises when data and information for a place are limited, as is commonly the case, and this creates decision paralysis while more information is generated. Here, we discuss how ecological understanding of network interactions in coastal marine ecosystems can be used as a lens to bring together multiple lines of evidence and create actions. We list and describe four characteristics of marine ecosystem interaction networks including the possibility for; 1) indirect effects, 2) effects that emerge as stressor magnitude increases the number of network components implicated, 3) network interactions that amplify these indirect effects, and 4) feedbacks that reinforce or stabilise against indirect effects. We then link these four characteristics to three case studies of common coastal environmental issues to demonstrate how a general understanding of ecological interaction networks can enhance priorities for stressor management that can be applied even when specific data is limited.

Chemical Mixtures and Multiple Stressors: Same but Different?

Ecosystems are strongly influenced by multiple anthropogenic stressors, including a wide range of chemicals and their mixtures. Studies on the effects of multiple stressors have largely focussed on nonchemical stressors, whereas studies on chemical mixtures have largely ignored other stressors. However, both research areas face similar challenges and require similar tools and methods to predict the joint effects of chemicals or nonchemical stressors, and frameworks to integrate multiple chemical and nonchemical stressors are missing. We provide an overview of the research paradigms, tools, and methods commonly used in multiple stressor and chemical mixture research and discuss potential domains of cross-fertilization and joint challenges. First, we compare the general paradigms of ecotoxicology and (applied) ecology to explain the historical divide. Subsequently, we compare methods and approaches for the identification of interactions, stressor characterization, and designing experiments. We suggest that both multiple stressor and chemical mixture research are too focused on interactions and would benefit from integration regarding null model selection. Stressor characterization is typically more costly for chemical mixtures. While for chemical mixtures comprehensive classification systems at suborganismal level have been developed, recent classification systems for multiple stressors account for environmental context. Both research areas suffer from rather simplified experimental designs that focus on only a limited number of stressors, chemicals, and treatments. We discuss concepts that can guide more realistic designs capturing spatiotemporal stressor dynamics. We suggest that process-based and data-driven models are particularly promising to tackle the challenge of prediction of effects of chemical mixtures and nonchemical stressors on (meta-)communities and (meta-)food webs. We propose a framework to integrate the assessment of effects for multiple stressors and chemical mixtures. Environ Toxicol Chem 2023;42:1915-1936. © 2023 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Spatial variability of the Po River food web and its comparison with the Danube River food web

Freshwater ecosystems are experiencing unprecedented pressure globally. To address environmental challenges, systematic and comparative studies on ecosystems are needed, though mostly lacking, especially for rivers. Here, we describe the food web of the Po River (as integrated from the white literature and monitoring data), describe the three river sections using network analysis, and compare our results with the previously compiled Danube River food web. The Po River food web was taxonomically aggregated in five consecutive steps (T1-T5) and it was also analyzed using the regular equivalence (REGE) algorithm to identify structurally similar nodes in the most aggregated T5 model. In total, the two river food webs shared 30 nodes. Two network metrics (normalized degree centrality [nDC]) and normalized betweenness centrality [nBC]) were compared using Mann-Whitney tests in the two rivers. On average, the Po River nodes have larger nDC values than in the Danube, meaning that neighboring connections are better mapped. Regarding nBC, there were no significant differences between the two rivers. Finally, based on both centrality indices, Carassius auratus is the most important node in the Po River food web, whereas phytoplankton and detritus are most important in the Danube River. Using network analysis and comparative methods, it is possible to draw attention to important trophic groups and knowledge gaps, which can guide future research. These simple models for the Po River food web can pave the way for more advanced models, supporting quantitative and predictive-as well as more functional-descriptions of ecosystems.

Integrating the flow regime and water quality effects into a niche-based metacommunity dynamics model for river ecosystems

Aquatic community dynamics are closely dominated by flow regime and water quality conditions, which are increasingly threatened by dam regulation, water diversion, and nutrition pollution. However, further understanding of the ecological impacts of flow regime and water quality conditions on aquatic multi-population dynamics has rarely been integrated into existing ecological models. To address this issue, a new niche-based metacommunity dynamics model (MDM) is proposed. The MDM aims to simulate the coevolution processes of multiple populations under changing abiotic environments, pioneeringly applied to the mid-lower Han River, China. The quantile regression method was used for the first time to derive ecological niches and competition coefficients of the MDM, which are demonstrated to be reasonable by comparing them with the empirical evidence. Simulation results show that the Nash efficiency coefficients for fish, zooplankton, zoobenthos, and macrophytes are more than 0.64, while the Pearson correlation coefficients for them are no less than 0.71. Overall, the MDM performs effectively in simulating metacommunity dynamics. For all river stations, the average contributions of biological interaction, flow regime effects, and water quality effects to multi-population dynamics are 64%, 21%, and 15%, respectively, suggesting that the population dynamics are dominated by biological interaction. For upstream stations, the fish population is 8%-22% more responsive to flow regime alteration than other populations, while other populations are 9%-26% more responsive to changes in water quality conditions than fish. For downstream stations, flow regime effects on each population account for less than 1% due to more stable hydrological conditions. The innovative contribution of this study lies in proposing a multi-population model to quantify the effects of flow regime and water quality on aquatic community dynamics by incorporating multiple indicators of water quantity, water quality, and biomass. This work has potential for the ecological restoration of rivers at the ecosystem level. This study also highlights the importance of considering threshold and tipping point issues when analyzing the "water quantity-water quality-aquatic ecology" nexus in future works.

Identifying historical and future global change drivers that place species recovery at risk

Ecosystem management in the face of global change requires understanding how co-occurring threats affect species and communities. Such an understanding allows for effective management strategies to be identified and implemented. An important component of this is differentiating between factors that are within (e.g. invasive predators) or outside (e.g. drought, large wildfires) of a local manager's control. In the global biodiversity hotspot of south-western Australia, small- and medium-sized mammal species are severely affected by anthropogenic threats and environmental disturbances, including invasive predators, fire, and declining rainfall. However, the relative importance of different drivers has not been quantified. We used data from a long-term monitoring program to fit Bayesian state-space models that estimated spatial and temporal changes in the relative abundance of four threatened mammal species: the woylie (Bettongia penicillata), chuditch (Dasyurus geoffroii), koomal (Trichosurus vulpecula) and quenda (Isoodon fusciventor). We then use Bayesian structural equation modelling to identify the direct and indirect drivers of population changes, and scenario analysis to forecast population responses to future environmental change. We found that habitat loss or conversion and reduced primary productivity (caused by rainfall declines) had greater effects on species' spatial and temporal population change than the range of fire and invasive predator (the red fox Vulpes vulpes) management actions observed in the study area. Scenario analysis revealed that a greater extent of severe fire and further rainfall declines predicted under climate change, operating in concert are likely to further reduce the abundance of these species, but may be mitigated partially by invasive predator control. Considering both historical and future drivers of population change is necessary to identify the factors that risk species recovery. Given that both anthropogenic pressures and environmental disturbances can undermine conservation efforts, managers must consider how the relative benefit of conservation actions will be shaped by ongoing global change.

Earth Systems to Anthropocene Systems: An Evolutionary, System-of-Systems, Convergence Paradigm for Interdependent Societal Challenges

Humans have made profound changes to the Earth. The resulting societal challenges of the Anthropocene (e.g., climate change and impacts, renewable energy, adaptive infrastructure, disasters, pandemics, food insecurity, and biodiversity loss) are complex and systemic, with causes, interactions, and consequences that cascade across a globally connected system of systems. In this Critical Review, we turn to our "origin story" for insight, briefly tracing the formation of the Universe and the Earth, the emergence of life, the evolution of multicellular organisms, mammals, primates, and humans, as well as the more recent societal transitions involving agriculture, urbanization, industrialization, and computerization. Focusing on the evolution of the Earth, genetic evolution, the evolution of the brain, and cultural evolution, which includes technological evolution, we identify a nested evolutionary sequence of geophysical, biophysical, sociocultural, and sociotechnical systems, emphasizing the causal mechanisms that first formed, and then transformed, Earth systems into Anthropocene systems. Describing how the Anthropocene systems coevolved, and briefly illustrating how the ensuing societal challenges became tightly integrated across multiple spatial, temporal, and organizational scales, we conclude by proposing an evolutionary, system-of-systems, convergence paradigm for the entire family of interdependent societal challenges of the Anthropocene.

Evaluating red tide effects on the West Florida Shelf using a spatiotemporal ecosystem modeling framework

The West Florida Shelf (WFS), located in the eastern Gulf of Mexico, fosters high species richness and supports highly valuable fisheries. However, red tide events occur regularly that can impact fisheries resources as well as ecosystem state, functioning, and derived services. Therefore, it is important to evaluate and quantify the spatiotemporal impacts of red tides to improve population assessments, mitigate potential negative effects through management, and better understand disturbances to support an ecosystem-based management framework. To model red tide effects on the marine community, we used Ecospace, the spatiotemporal module of the ecosystem modeling framework Ecopath with Ecosim. The inclusion of both lethal and sublethal response functions to red tide and a comprehensive calibration procedure allowed to systematically evaluate red tide effects and increased the robustness of the model and management applicability. Our results suggest severe red tide impacts have occurred on the WFS at the ecosystem, community, and population levels in terms of biomass, catch, and productivity. Sublethal and indirect food-web effects of red tide triggered compensatory responses such as avoidance behavior and release from predation and/or competition.. This study represents a step forward to operationalize spatiotemporal ecosystem models for management purposes that may increase the ability of fisheries managers to respond more effectively and be more proactive to episodic mortality events, such as those caused by red tides.

Spike in pollution to ignite the bursting of COVID-19 second wave is more dangerous than spike of SAR-CoV-2 under environmental ignorance in long term: a review

Specific areas in many countries such as Italy, India, China, Brazil, Germany and the USA have witnessed that air pollution increases the risk of COVID-19 severity as particulate matters transmit the virus SARS-CoV-2 and causes high expression of ACE2, the receptor for spike protein of the virus, especially under exposure to NO₂, SO₂ and NOx emissions. Wastewater-based epidemiology of COVID-19 is also noticed in many countries such as the Netherlands, the USA, Paris, France, Australia, Spain, Italy, Switzerland China, India and Hungary. Soil is also found to be contaminated by the RNA of SARS-CoV-2. Activities including defecation and urination by infected people contribute to the source for soil contamination, while release of wastewater containing cough, urine and stool of infected people from hospitals and home isolation contributes to the source of SARS-CoV-2 RNA in both water and soil. Detection of the virus early before the outbreak of the disease supports this fact. Based on this information, spike in pollution is found to be more dangerous in long-term than the spike protein of SARS-CoV-2. It is because the later one may be controlled in future within months or few years by vaccination and with specific drugs, but the former one provides base for many diseases including the current and any future pandemics. Although such predictions and the positive effects of SARS-CoV-2 on environment was already forecasted after the first wave of COVID-19, the learnt lesson as spotlight was not considered as one of the measures for which 2nd wave has quickly hit the world.

Comparison of three modelling frameworks for aquatic ecosystems: practical aspects and applicability

Freshwater ecosystems are under multiple stressors and it is crucial to find methods to better describe, manage, and sustain aquatic ecosystems. Ecosystem modelling has become an important tool in integrating trophic relationships into food webs, assessing important nodes using network analysis, and making predictions via simulations. Fortunately, several modelling techniques exist, but the question is which approach is relevant and applicable when? In this study, we compare three modelling frameworks (Ecopath, Loop Analysis in R, STELLA software) using a case study of a small aquatic network (8 nodes). The choice of framework depends on the research question and data availability. We approach this topic from a methodological aspect by describing the data requirements and by comparing the applicability and limitations of each modelling approach. Each modelling framework has its specific focus, but some functionalities and outcomes can be compared. The predictions of Loop Analysis as compared to Ecopath’s Mixed Trophic Impact plot are in good agreement at the top and bottom trophic levels, but the middle trophic levels are less similar. This suggests that further comparisons are needed of networks of varying resolution and size. Generally, when data are limiting, Loop Analysis can provide qualitative predictions, while the other two methods provide quantitative results, yet rely on more data.

Near-term phytoplankton forecasts reveal the effects of model time step and forecast horizon on predictability

As climate and land use increase the variability of many ecosystems, forecasts of ecological variables are needed to inform management and use of ecosystem services. In particular, forecasts of phytoplankton would be especially useful for drinking water management, as phytoplankton populations are exhibiting greater fluctuations due to human activities. While phytoplankton forecasts are increasing in number, many questions remain regarding the optimal model time step (the temporal frequency of the forecast model output), time horizon (the length of time into the future a prediction is made) for maximizing forecast performance, as well as what factors contribute to uncertainty in forecasts and their scalability among sites. To answer these questions, we developed near-term, iterative forecasts of phytoplankton 1-14 days into the future using forecast models with three different time steps (daily, weekly, fortnightly), that included a full uncertainty partitioning analysis at two drinking water reservoirs. We found that forecast accuracy varies with model time step and forecast horizon, and that forecast models can outperform null estimates under most conditions. Weekly and fortnightly forecasts consistently outperformed daily forecasts at 7-day and 14-day horizons, a trend that increased up to the 14-day forecast horizon. Importantly, our work suggests that forecast accuracy can be increased by matching the forecast model time step to the forecast horizon for which predictions are needed. We found that model process uncertainty was the primary source of uncertainty in our phytoplankton forecasts over the forecast period, but parameter uncertainty increased during phytoplankton blooms and when scaling the forecast model to a new site. Overall, our scalability analysis shows promising results that simple models can be transferred to produce forecasts at additional sites. Altogether, our study advances our understanding of how forecast model time step and forecast horizon influence the forecastability of phytoplankton dynamics in aquatic systems and adds to the growing body of work regarding the predictability of ecological systems broadly.

Resolving ecosystem complexity in ecological risk assessment for mine site rehabilitation

CONTEXT

Ecological Risk Assessments (ERAs) are important tools for supporting evidence-based decision making. However, most ERA frameworks rarely consider complex ecological feedbacks, which limit their capacity to evaluate risks at community and ecosystem levels of organisation.

METHOD

We used qualitative mathematical modelling to add additional perspectives to previously conducted ERAs for the rehabilitation of the Ranger uranium mine (Northern Territory, Australia) and support an assessment of the cumulative risks from the mine site. Using expert elicitation workshops, separate qualitative models and scenarios were developed for aquatic and terrestrial systems. The models developed in the workshops were used to construct Bayes Nets that predicted whole-of-ecosystem outcomes after components were perturbed.

RESULTS

The terrestrial model considered the effect of fire and weeds on established native vegetation that will be important for the successful rehabilitation of Ranger. It predicted that a combined intervention that suppresses both weeds and fire intensity gave similar response predictions as for weed control alone, except for lower levels of certainty to tall grasses and fire intensity in models with immature trees or tall grasses. However, this had ambiguous predictions for short grasses and forbs, and tall grasses in models representing mature vegetation. The aquatic model considered the effects of magnesium (Mg), a key solute in current and predicted mine runoff and groundwater egress, which is known to adversely affect many aquatic species. The aquatic models provided support that attached algae and phytoplankton assemblages are the key trophic base for food webs. It predicted that shifts in phytoplankton abundance arising from increase in Mg to receiving waters, may result in cascading effects through the food-chain.

CONCLUSION

The qualitative modelling approach was flexible and capable of modelling both gradual (i.e. decadal) processes in the mine-site restoration and the comparatively more rapid (seasonal) processes of the aquatic ecosystem. The modelling also provides a useful decision tool for identifying important ecosystem sub-systems for further research efforts.

Analysis of sloppiness in model simulations: Unveiling parameter uncertainty when mathematical models are fitted to data

This work introduces a comprehensive approach to assess the sensitivity of model outputs to changes in parameter values, constrained by the combination of prior beliefs and data. This approach identifies stiff parameter combinations strongly affecting the quality of the model-data fit while simultaneously revealing which of these key parameter combinations are informed primarily by the data or are also substantively influenced by the priors. We focus on the very common context in complex systems where the amount and quality of data are low compared to the number of model parameters to be collectively estimated, and showcase the benefits of this technique for applications in biochemistry, ecology, and cardiac electrophysiology. We also show how stiff parameter combinations, once identified, uncover controlling mechanisms underlying the system being modeled and inform which of the model parameters need to be prioritized in future experiments for improved parameter inference from collective model-data fitting.

Combining food web theory and population dynamics to assess the impact of invasive species

The impacts of invasive species on resident communities are driven by a tangle of ecological interactions difficult to quantify empirically. Combining a niche model with a population dynamic model, both allometrically parametrized, may represent a consistent framework to investigate invasive species impacts on resident communities in a food web context when empirical data are scarce. We used this framework to assess the ecological consequences of an invasive apex predator (Silurus glanis) in peri-Alpine lake food webs. Both increases and decreases of resident species abundances were highlighted and differed when accounting for different S. glanis body sizes. Complementarily, the prominence of indirect effects, such as trophic cascades, suggested that common approaches may only capture a restricted fraction of invasion consequences through direct predation or competition. By leveraging widely available biodiversity data, our approach may provide relevant insights for a comprehensive assessment and management of invasive species impacts on aquatic ecosystems.

No-take marine protected areas enhance the benefits of kelp-forest restoration for fish but not fisheries

Kelp habitat restoration is gaining traction as a management action to support recovery in areas affected by severe disturbances, thereby ensuring the sustainability of ecosystem services. Knowing when and where to restore is a major question. Using a single-species population model, we consider how restoring inside marine protected areas (MPAs) might benefit coastal fish populations and fisheries. We found that MPAs can greatly enhance the population benefits of restoration but at a small cost to fishery yields. Generally, restoring inside MPAs had a better overall gains-loss outcome, especially if the system is under high fishing pressure or severe habitat loss. However, restoring outside became preferable when predatory fish indirectly benefit kelp habitats. In either case, successful restoration actions may be difficult to detect in time-series data due to complex transient dynamics. We provide context for setting management goals and social expectations for the ecosystem service implications of restoration in MPAs.

Estimating the Impacts of Plant Internal Nitrogen Deficit at Key Top Dressing Stages on Corn Productivity and Intercepted Photosynthetic Active Radiation

Accurate and timely appraisal of plant nitrogen (N) demand is imperative to regulate the canopy structure and corn production. The strength and time of plant N deficit can be quantified by critical N concentration. The study was aimed to analyze nitrogen nutrition index (NNI), nitrogen deficit content (NDC), plant nitrogen productivity (PNP), and a fraction of intercepted photosynthetic active radiation (FIPAR) across different N treatments and to develop NNI-NDC, NNI-PNP, NNI-FIPAR, NDC-PNP, and NDC-FIPAR relationships from V6 to V12 stages of corn to quantify the suitable PNP and FIPAR values under the optimal plant N condition. Four multi-N rates (0, 75, 90, 150, 180, 225, 270, and 300 kg N ha^-1) field experiments were conducted with two cultivars of corn in Henan province of China. Results indicated that N fertilization affected yield, plant biomass, plant N content, and leaf area index. The values of NNI and NDC were from 0.54 to 1.28 kg ha^-1 and from -28.13 to 21.99 kg ha^-1 under the different treatments of N rate, respectively. The NDC and NNI showed significantly negative relationships from V6 to V12 stages. The values of PNP and FIPAR increased gradually with the crop growth process. The PNP values gradually declined while the FIPAR values of every leaf layer increased with the increase of N supply. The NDC-PNP and NNI-FIPAR relationships were significantly positive; however, the relationships between NNI-PNP and NDC-FIPAR were significantly negative during the vegetative period of corn. The coefficient of determination (R ²) based on NNI was better than that on NDC. The FIPAR values were ~0.35, 0.67, and 0.76% at the upper, middle, and bottom of leaf layers, respectively, and PNP values were ~39, 44, and 51 kg kg^-1 at V6, V9, and V12 stages, respectively, when NNI and NDC values were equal to 1 and 0 kg ha^-1, respectively. This study described the quantitative information about the effect of a plant's internal N deficit on plant N productivity and canopy light intercept. The projected results would assist in predicting the appropriate plant growth status during key N top-dressing stages of corn, which can optimize N application and improve N use efficiency.

Unlocking Drought-Induced Tree Mortality: Physiological Mechanisms to Modeling

Drought-related tree mortality has become a major concern worldwide due to its pronounced negative impacts on the functioning and sustainability of forest ecosystems. However, our ability to identify the species that are most vulnerable to drought, and to pinpoint the spatial and temporal patterns of mortality events, is still limited. Model is useful tools to capture the dynamics of vegetation at spatiotemporal scales, yet contemporary land surface models (LSMs) are often incapable of predicting the response of vegetation to environmental perturbations with sufficient accuracy, especially under stressful conditions such as drought. Significant progress has been made regarding the physiological mechanisms underpinning plant drought response in the past decade, and plant hydraulic dysfunction has emerged as a key determinant for tree death due to water shortage. The identification of pivotal physiological events and relevant plant traits may facilitate forecasting tree mortality through a mechanistic approach, with improved precision. In this review, we (1) summarize current understanding of physiological mechanisms leading to tree death, (2) describe the functionality of key hydraulic traits that are involved in the process of hydraulic dysfunction, and (3) outline their roles in improving the representation of hydraulic function in LSMs. We urge potential future research on detailed hydraulic processes under drought, pinpointing corresponding functional traits, as well as understanding traits variation across and within species, for a better representation of drought-induced tree mortality in models.

Modeling total predation to avoid perverse outcomes from cat control in a data-poor island ecosystem

Data hungry, complex ecosystem models are often used to predict the consequences of threatened species management, including perverse outcomes. Unfortunately, this approach is impractical in many systems, which have insufficient data to parameterize ecosystem interactions or reliably calibrate or validate such models. Here we demonstrate a different approach, using a minimum realistic model to guide decisions in data- and resource-scarce systems. We illustrate our approach with a case-study in an invaded ecosystem from Christmas Island, Australia, where there are concerns that cat eradication to protect native species, including the red-tailed tropicbird, could release meso-predation by invasive rats. We use biophysical constraints (metabolic demand) and observable parameters (e.g. prey preferences) to assess the combined cat and rat abundances which would threaten the tropicbird population. We find that the population of tropicbirds cannot be sustained if predated by 1607 rats (95% credible interval (CI) [103, 5910]) in the absence of cats, or 21 cats (95% CI [2, 82]) in the absence of rats. For every cat removed from the island, the bird's net population growth rate improves, provided that the rats do not increase by more than 77 individuals (95% CI [30, 174]). Thus, in this context, one cat is equivalent to 30-174 rats. Our methods are especially useful for on-the-ground predator control in the absence of knowledge of predator-predator interactions, to assess whether 1) the current abundance of predators threatens the prey population of interest, 2) managing one predator species alone is sufficient to protect the prey species given potential release of another predator, and 3) control of multiple predator species is needed to meet the conservation goal. Our approach demonstrates how to use limited information for maximum value in data-poor systems, by shifting the focus from predicting future trajectories, to identifying conditions which threaten the conservation goal. This article is protected by copyright. All rights reserved.

Evaluating unintended consequences of intentional species introductions and eradications for improved conservation management

Increasingly intensive strategies to maintain biodiversity and ecosystem function are being deployed in response to global anthropogenic threats, including intentionally introducing and eradicating species via assisted migration, rewilding, biological control, invasive species eradications, and gene drives. These actions are highly contentious because of their potential for unintended consequences. We conducted a global literature review of these conservation actions to quantify how often unintended outcomes occur and to elucidate their underlying causes. To evaluate conservation outcomes, we developed a community assessment framework for systematically mapping the range of possible interaction types for 111 case studies. Applying this tool, we quantified the number of interaction types considered in each study and documented the nature and strength of intended and unintended outcomes. Intended outcomes were reported in 51% of cases, a combination of intended outcomes and unintended outcomes in 26%, and strictly unintended outcomes in 10%. Hence, unintended outcomes were reported in 36% of all cases evaluated. In evaluating overall conservations outcomes (weighing intended vs. unintended effects), some unintended effects were fairly innocuous relative to the conservation objective, whereas others resulted in serious unintended consequences in recipient communities. Studies that assessed a greater number of community interactions with the target species reported unintended outcomes more often, suggesting that unintended consequences may be underreported due to insufficient vetting. Most reported unintended outcomes arose from direct effects (68%) or simple density-mediated or indirect effects (25%) linked to the target species. Only a few documented cases arose from more complex interaction pathways (7%). Therefore, most unintended outcomes involved simple interactions that could be predicted and mitigated through more formal vetting. Our community assessment framework provides a tool for screening future conservation actions by mapping the recipient community interaction web to identify and mitigate unintended outcomes from intentional species introductions and eradications for conservation.

Habitat mediates coevolved but not novel species interactions

Ongoing recovery of native predators has the potential to alter species interactions, with community and ecosystem wide implications. We estimated the co-occurrence of three species of conservation and management interest from a multi-species citizen science camera trap survey. We demonstrate fundamental differences in novel and coevolved predator-prey interactions that are mediated by habitat. Specifically, we demonstrate that anthropogenic habitat modification had no influence on the expansion of the recovering native pine marten in Ireland, nor does it affect the predator's suppressive influence on an invasive prey species, the grey squirrel. By contrast, the direction of the interaction between the pine marten and a native prey species, the red squirrel, is dependent on habitat. Pine martens had a positive influence on red squirrel occurrence at a landscape scale, especially in native broadleaf woodlands. However, in areas dominated by non-native conifer plantations, the pine marten reduced red squirrel occurrence. These findings suggest that following the recovery of a native predator, the benefits of competitive release are spatially structured and habitat-specific. The potential for past and future landscape modification to alter established interactions between predators and prey has global implications in the context of the ongoing recovery of predator populations in human-modified landscapes.

Marine heatwave challenges solutions to human-wildlife conflict

Despite the increasing frequency and magnitude of extreme climate events, little is known about how their impacts flow through social and ecological systems or whether management actions can dampen deleterious effects. We examined how the record 2014-2016 Northeast Pacific marine heatwave influenced trade-offs in managing conflict between conservation goals and human activities using a case study on large whale entanglements in the U.S. west coast's most lucrative fishery (the Dungeness crab fishery). We showed that this extreme climate event diminished the power of multiple management strategies to resolve trade-offs between entanglement risk and fishery revenue, transforming near win-win to clear win-lose outcomes (for whales and fishers, respectively). While some actions were more cost-effective than others, there was no silver-bullet strategy to reduce the severity of these trade-offs. Our study highlights how extreme climate events can exacerbate human-wildlife conflict, and emphasizes the need for innovative management and policy interventions that provide ecologically and socially sustainable solutions in an era of rapid environmental change.

Predicting the sign of trophic effects: individual-based simulation versus loop analysis

Food web research needs to be predictive in order to support decisions system-based conservation. In order to increase predictability and applicability, complexity needs to be managed in such a way that we are able to provide simple and clear results. One question emerging frequently is whether certain perturbations (environmental effects or human impact) have positive or negative effects on natural ecosystems or their particular components. Yet, most of food web studies do not consider the sign of effects. Here, we study 6 versions of the Kelian River (Borneo) food web, representing six study sites along the river. For each network, we study the signs of the effects of a perturbed trophic group i on each other j groups. We compare the outcome of the relatively complicated dynamical simulation model and the relatively simple loop analysis model. We compare these results for the 6 sites and also the 14 trophic groups. Finally, we see if sign-agreement and sign-determinacy depend on certain structural features (node centrality, interaction strength). We found major differences between different modelling scenarios, with herbivore-detritivore fish behaving in the most consistent, while algae and particulate organic matter behaving in the least consistent way. We also found higher agreement between the signs of predictions for trophic groups at higher trophic levels in sites 1–3 and at lower trophic levels in site 4–6. This means that the behaviour of predators in the more natural sections of the river and that of producers at the more human-impacted sections are more consistently predicted. This suggests to be more careful with the less consistently predictable trophic groups in conservation management.

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.

Southern Ocean Food Web Modelling: Progress, Prognoses, and Future Priorities for Research and Policy Makers

Graphical summary of multiple aspects of Southern Ocean food web structure and function including alternative energy pathways through pelagic food webs, climate change and fisheries impacts and the importance of microbial networks and benthic systems.

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An Expanded Framework for Community Viability Analysis

Community viability analysis (CVA) has been put forth as an analogue for population viability analysis (PVA), an accepted conservation tool for evaluating species-specific threat and management scenarios. The original proposal recommended that CVAs examine resistance-based questions. PVAs, however, are broadly applicable to multiple types of viability questions, suggesting that the original CVA definition may be too narrow. In the present article, we advance an expanded framework in which CVA includes any analysis assessing the status, threats, or management options of an ecological community. We discuss viability questions that can be investigated with CVA. We group those inquiries into categories of resistance, resilience, and persistence, and provide case studies for each. Finally, we broadly present the steps in a CVA.