Integrating impacts of climate change on aquatic environments in inter-basin water regulation: Establishing a critical threshold for best management practices

Inter-basin water diversion (IBWD) is a viable strategy to tackle water scarcity and quality degradation due to climate change and increasing water demand in headwaters regions. Nevertheless, the capacity of IBWD to mitigate the impacts of climate change on water quality has rarely been quantified, and the underlying processes are not well understood. Therefore, this study aims to elucidate how the IBWD manipulated total phosphorus (TP) loading dilution and conveying patterns under climate change and determine a critical threshold for the quantity of water entering downstream reservoirs (WIN) for operational scheduling. To resolve this issue, climate-driven hydrologic variability over a 60-year period was derived utilizing the least square fitting approach. Subsequently, six scenarios evaluating the response of in-lake TP concentrations (TPL) to increased temperatures and IBWDs of 50 %, 100 %, and 150 % from the baseline water volume in 2030 and 2050 were studied by employing a calibrated hydrological-water quality model (SWAT-YRWQM). In the next stage, three datasets derived from mathematical statistics based on the observed data, the Vollenweider formula, and modeled projections were integrated to formulate best management practices. The results revealed that elevated air temperatures would lead to reduced annual catchment runoff but increased IBWD. Additionally, our study quantified the IBWD potential for mitigating water quality degradation, indicating the adverse effects of climate change on TPL would be weakened by 4.2-14.4 %. A critical threshold for WIN was also quantified at 617 million m3, maintaining WIN at or near 617 million m3 through optimized operational scheduling of IBWD could effectively restrict external inflow TP loading to lower levels. This study clearly illustrates the intricate interactive effects of climate change and IBWD on aquatic environments. The methodology elucidated in this study for determining the critical threshold of WIN could be applied in water management for analogous watershed-receiving waterbody systems.

Diversification of refugia types needed to secure the future of coral reefs subject to climate change

Identifying locations of refugia from the thermal stresses of climate change for coral reefs and better managing them is one of the key recommendations for climate change adaptation. We review and summarize approximately 30 years of applied research focused on identifying climate refugia to prioritize the conservation actions for coral reefs under rapid climate change. We found that currently proposed climate refugia and the locations predicted to avoid future coral losses are highly reliant on  excess heat metrics, such as degree heating weeks. However, many existing alternative environmental, ecological, and life-history variables could be used to identify other types of refugia that lead to the desired diversified portfolio for coral reef conservation. To improve conservation priorities for coral reefs, there is a need to evaluate and validate the predictions of climate refugia with long-term field data on coral abundance, diversity, and functioning. There is also the need to identify and safeguard locations displaying resistance toprolonged exposure to heat waves and the ability to recover quickly after thermal exposure. We recommend using more metrics to identify a portfolio of potential refugia sites for coral reefs that can avoid, resist, and recover from exposure to high ocean temperatures and the consequences of climate change, thereby shifting past efforts focused on avoidance to a diversified risk-spreading portfolio that can be used to improve strategic coral reef conservation in a rapidly warming climate.

A synthesis of anthropogenic stress effects on emergence-mediated aquatic-terrestrial linkages and riparian food webs

Anthropogenic stress alters the linkage between aquatic and terrestrial ecosystems in various ways. Here, we review the contemporary literature on how alterations in aquatic systems through environmental pollution, invasive species and hydromorphological changes carry-over to terrestrial ecosystems and the food webs therein. We consider both the aquatic insect emergence and flooding as pathways through which stressors can propagate from the aquatic to the terrestrial system. We specifically synthesize and contextualize results on the roles of pollutants in the emergence pathway and their top-down consequences. Our review revealed that the emergence and flooding pathway are only considered in isolation and that the overall effects of invasive species or pollutants on food webs at the water-land interface require further attention. While very few recent studies looked at invasive species, a larger number of studies focused on metal transfer compared to pesticides, pharmaceuticals or PCBs, and multiple stress studies up to now left aquatic-terrestrial linkages unconsidered. Recent research on pollutants and emergence used aquatic-terrestrial mesocosms to elucidate the effects of aquatic stressors such as the mosquito control agent Bti, metals or pesticides to understand the effects on riparian spiders. Quality parameters, such as the structural and functional composition of emergent insect communities, the fatty acid profiles, yet also the composition of pollutants transferred to land prove to be important for the effects on riparian spiders. Process-based models including quality of emergence are useful to predict the resulting top-down directed food web effects in the terrestrial recipient ecosystem. In conclusion, we present and recommend a combination of empirical and modelling approaches in order to understand the complexity of aquatic-terrestrial stressor propagation and its spatial and temporal variation.

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.

Organic matter stability and lability in terrestrial and aquatic ecosystems: A chemical and microbial perspective

Terrestrial and aquatic ecosystems have specific carbon fingerprints and sequestration potential, due to the intrinsic properties of the organic matter (OM), mineral content, environmental conditions, and microbial community composition and functions. A small variation in the OM pool can imbalance the carbon dynamics that ultimately affect the climate and functionality of each ecosystem, at regional and global scales. Here, we review the factors that continuously contribute to carbon stability and lability, with particular attention to the OM formation and nature, as well as the microbial activities that drive OM aggregation, degradation and eventually greenhouse gas emissions. We identified that in both aquatic and terrestrial ecosystems, microbial attributes (i.e., carbon metabolism, carbon use efficiency, necromass, enzymatic activities) play a pivotal role in transforming the carbon stock and yet they are far from being completely characterised and not often included in carbon estimations. Therefore, future research must focus on the integration of microbial components into carbon mapping and models, as well as on translating molecular-scaled studies into practical approaches. These strategies will improve carbon management and restoration across ecosystems and contribute to overcome current climate challenges.

Sustainable transformation of agriculture requires landscape experiments

Transformation of agriculture to realise sustainable site-specific management requires comprehensive scientific support based on field experiments to capture the complex agroecological process, incite new policies and integrate them into farmers' decisions. However, current experimental approaches are limited in addressing the wide spectrum of sustainable agroecosystem and landscape characteristics and in supplying stakeholders with suitable solutions and measures. This review identifies major constraints in current field experimentation, such as a lack of consideration of multiple processes and scales and a limited ability to address interactions between them. It emphasizes the urgent need to establish a new category of landscape experimentation that empowers agricultural research on sustainable agricultural systems, aiming at elucidating interactions among various landscape structures and functions, encompassing both natural and anthropogenic features. It extensively discusses the key characteristics of landscape experiments and major opportunities to include them in the agricultural research agenda. In particular, simultaneously considering multiple factors, and thus processes at different scales and possible synergies or antagonisms among them would boost our understanding of heterogeneous agricultural landscapes. We also highlight that though various studies identified promising approaches with respect to experimental design and data analysis, further developments are still required to build a fully functional and integrated framework for landscape experimentation in agricultural settings.

Regime shifts in shallow lakes explained by critical turbidity

Worldwide, water quality managers target a clear, macrophyte-dominated state over a turbid, phytoplankton-dominated state in shallow lakes. The competition mechanisms underlying these ecological states were explored in the 1990s, but the concept of critical turbidity seems neglected in contemporary water quality models. In particular, a simple mechanistic model of alternative stable states in shallow lakes accounting for resource competition mechanisms and critical turbidity is lacking. To this end, we combined Scheffer's theory on critical turbidity with insights from nutrient and light competition theory founded by Tilman, Huisman and Weissing. This resulted in a novel graphical and mathematical model, GPLake-M, that is relatively simple and mechanistically understandable and yet captures the essential mechanisms leading to alternative stable states in shallow lakes. The process-based PCLake model was used to parameterize the model parameters and to test GPLake-M using a pattern-oriented strategy. GPLake-M's application range and position in the model spectrum are discussed. We believe that our results support the fundamental understanding of regime shifts in shallow lakes and provide a starting point for further mechanistic and management-focused explorations and model development. Furthermore, the concept of critical turbidity and the relation between light-limited submerged macrophytes and nutrient-limited phytoplankton might provide a new focus for empirical aquatic ecological research and water quality monitoring programs.

Demography-environment relationships improve mechanistic understanding of range dynamics under climate change

Species respond to climate change with range and abundance dynamics. To better explain and predict them, we need a mechanistic understanding of how the underlying demographic processes are shaped by climatic conditions. Here, we aim to infer demography-climate relationships from distribution and abundance data. For this, we developed spatially explicit, process-based models for eight Swiss breeding bird populations. These jointly consider dispersal, population dynamics and the climate-dependence of three demographic processes-juvenile survival, adult survival and fecundity. The models were calibrated to 267 nationwide abundance time series in a Bayesian framework. The fitted models showed moderate to excellent goodness-of-fit and discriminatory power. The most influential climatic predictors for population performance were the mean breeding-season temperature and the total winter precipitation. Contemporary climate change benefitted the population trends of typical mountain birds leading to lower population losses or even slight increases, whereas lowland birds were adversely affected. Our results emphasize that generic process-based models embedded in a robust statistical framework can improve our predictions of range dynamics and may allow disentangling of the underlying processes. For future research, we advocate a stronger integration of experimental and empirical studies in order to gain more precise insights into the mechanisms by which climate affects populations. This article is part of the theme issue 'Detecting and attributing the causes of biodiversity change: needs, gaps and solutions'.

The influence of prey availability on behavioral decisions and reproductive success of a central-place forager during lactation

Marine central-place foragers are increasingly faced with altered prey landscapes, necessitating predictions of the impact of such changes on behavior, reproductive success, and population dynamics. We used state-dependent behavioral life history theory implemented via Stochastic Dynamic Programming (SDP) to explore the influence of changes in prey distribution and energy gain from foraging on the behavior and reproductive success of a central place forager during lactation. Our work is motivated by northern fur seals (Callorhinus ursinus) because of the ongoing population decline of the Eastern Pacific stock and projected declines in biomass of walleye pollock (Gadus chalcogrammus), a key fur seal prey species in the eastern Bering Sea. We also explored how changes in female and pup metabolic rates, body size, and lactation duration affected model output to provide insight into traits that might experience selective pressure in response to reductions in prey availability. Simulated females adopted a central-place foraging strategy after an initial extended period spent on land (4.7-8.3 days). Trip durations increased as the high energy prey patch moved farther from land or when the energy gain from foraging decreased. Increases in trip duration adversely affected pup growth rates and wean mass despite attempts to compensate by increasing land durations. Metabolic rate changes had the largest impacts on pup wean mass, with reductions in a pup's metabolic rate allowing females to successfully forage at distances of 600+ km from land for up to 15+ days. Our results indicate that without physiological adaptations, a rookery is unlikely to be viable if the primary foraging grounds are 400 km or farther from the rookery. To achieve pup growth rates characteristic of a population experiencing rapid growth, model results indicate the primary foraging grounds need to be <150 km from the rookery.

Conceptualizing microbe-plasmid communities as complex adaptive systems

Plasmids shape microbial communities' diversity, structure, and function. Nevertheless, we lack a mechanistic understanding of how community structure and dynamics emerge from local microbe-plasmid interactions and coevolution. Addressing this gap is challenging because multiple processes operate simultaneously at multiple levels of organization. For example, immunity operates between a plasmid and a cell, but incompatibility mechanisms regulate coexistence between plasmids. Conceptualizing microbe-plasmid communities as complex adaptive systems is a promising approach to overcoming these challenges. I illustrate how agent-based evolutionary modeling, extended by network analysis, can be used to quantify the relative importance of local processes governing community dynamics. These theoretical developments can advance our understanding of plasmid ecology and evolution, especially when combined with empirical data.

Archetype models upscale understanding of natural pest control response to land-use change

Control of crop pests by shifting host plant availability and natural enemy activity at landscape scales has great potential to enhance the sustainability of agriculture. However, mainstreaming natural pest control requires improved understanding of how its benefits can be realized across a variety of agroecological contexts. Empirical studies suggest significant but highly variable responses of natural pest control to land-use change. Current ecological models are either too specific to provide insight across agroecosystems or too generic to guide management with actionable predictions. We suggest obtaining the full benefit of available empirical, theoretical, and methodological knowledge by combining trait-mediated understanding from correlative studies with the explicit representation of causal relationships achieved by mechanistic modeling. To link these frameworks, we adapt the concept of archetypes, or context-specific generalizations, from sustainability science. Similar responses of natural pest control to land-use gradients across cases that share key attributes, such as functional traits of focal organisms, indicate general processes that drive system behavior in a context-sensitive manner. Based on such observations of natural pest control, a systematic definition of archetypes can provide the basis for mechanistic models of intermediate generality that cover all major agroecosystems worldwide. Example applications demonstrate the potential for upscaling understanding and improving predictions of natural pest control, based on knowledge transfer and scientific synthesis. A broader application of this mechanistic archetype approach promises to enhance ecology's contribution to natural resource management across diverse regions and social-ecological contexts.

Quantifying the environmental limits to fire spread in grassy ecosystems

Modeling fire spread as an infection process is intuitive: An ignition lights a patch of fuel, which infects its neighbor, and so on. Infection models produce nonlinear thresholds, whereby fire spreads only when fuel connectivity and infection probability are sufficiently high. These thresholds are fundamental both to managing fire and to theoretical models of fire spread, whereas applied fire models more often apply quasi-empirical approaches. Here, we resolve this tension by quantifying thresholds in fire spread locally, using field data from individual fires (n = 1,131) in grassy ecosystems across a precipitation gradient (496 to 1,442 mm mean annual precipitation) and evaluating how these scaled regionally (across 533 sites) and across time (1989 to 2012 and 2016 to 2018) using data from Kruger National Park in South Africa. An infection model captured observed patterns in individual fire spread better than competing models. The proportion of the landscape that burned was well described by measurements of grass biomass, fuel moisture, and vapor pressure deficit. Regionally, averaging across variability resulted in quasi-linear patterns. Altogether, results suggest that models aiming to capture fire responses to global change should incorporate nonlinear fire spread thresholds but that linear approximations may sufficiently capture medium-term trends under a stationary climate.

Optimizing process-based models to predict current and future soil organic carbon stocks at high-resolution

From hillslope to small catchment scales (< 50 km²), soil carbon management and mitigation policies rely on estimates and projections of soil organic carbon (SOC) stocks. Here we apply a process-based modeling approach that parameterizes the MIcrobial-MIneral Carbon Stabilization (MIMICS) model with SOC measurements and remotely sensed environmental data from the Reynolds Creek Experimental Watershed in SW Idaho, USA. Calibrating model parameters reduced error between simulated and observed SOC stocks by 25%, relative to the initial parameter estimates and better captured local gradients in climate and productivity. The calibrated parameter ensemble was used to produce spatially continuous, high-resolution (10 m²) estimates of stocks and associated uncertainties of litter, microbial biomass, particulate, and protected SOC pools across the complex landscape. Subsequent projections of SOC response to idealized environmental disturbances illustrate the spatial complexity of potential SOC vulnerabilities across the watershed. Parametric uncertainty generated physicochemically protected soil C stocks that varied by a mean factor of 4.4 × across individual locations in the watershed and a − 14.9 to + 20.4% range in potential SOC stock response to idealized disturbances, illustrating the need for additional measurements of soil carbon fractions and their turnover time to improve confidence in the MIMICS simulations of SOC dynamics.

Models for Assessing Urban Ecosystem Services: Status and Outlooks

Urban ecosystem services provide many benefits for human beings. Given the dramatic increase of urbanization, maintaining sustainability of cities relies heavily on ecosystem services, and it is crucial for quantifying, managing, and optimizing urban ecosystem services to promote social and ecological sustainable development. This study presents the review of models for assessing urban ecosystem services through gathering the pertinent literatures which were published recent years. The main types of approaches for assessing urban ecosystem services were summarized, and the model simulation approach was detailed. From modelling techniques to the existing models, it was found that a process-based model is, relatively, a better way to detect the mechanism of urban ecological processes and simulate the future dynamic changes of urban ecosystem services. Three key limitations of existing products and frameworks were identified: (1) lacking understanding of multiple urban ecosystem services interactions, (2) ignoring accounting the socioeconomic factors into dynamics of urban ecosystem, and (3) lacking considerations of feedback effects between social system and urban ecosystem. The study concludes with outlooks that a comprehensive social-ecosystem model based on the social-ecological framework is helpful to reveal the relationships and interactions among various urban ecosystem services, and can better assess how human-induced urban growth affects ecosystem services, and better describe the feedback effect between the social environment and urban ecosystem services, as well as dynamically predict the changes of urban ecosystem services under different scenarios in future long time series.

Spatiotemporal dynamics drive synergism of land use and climatic extreme events in insect meta-populations

Ecosystems are increasingly threatened by co-occurring stressors associated with anthropogenic global change. Spatial stressor patterns range from local to regional to global, and temporal stressor patterns from discrete to continuous. To date, most multiple stressor studies covered short periods and focused on local effects and interactions. However, it remains largely unknown how stressors with different spatiotemporal patterns interact in their effects over longer periods. In particular, at higher spatial scales, biotic dynamics in ecological networks complicate the understanding of stressor interactions. We used a spatially explicit meta-population model for a generic freshwater insect, parameterized based on traits of the European damselfly Coenagrion mercuriale, to simulate scenarios of discrete climatic extreme events and continuous land use-related stress. Climatic extreme events were modeled as recurring mortality in all patches, whereas land use permanently influenced meta-populations via patch qualities and network connectivity. We found that the risk of discrete climatic extreme events to meta-populations depended strongly on the proportion of land use types, with effects ranging from negligible to extinction. Land use-related stress limited recovery in meta-populations from effects of climatic extreme events, resulting in synergistic stressor interactions. Moreover, the spatial configuration of land use type influenced the combined stressor effects with clustered configurations resulting in lower effects compared to a random configuration. Finally, we found that combined stressor effects can vary with the time point at which they were determined, indicating that inconclusive results in multiple stressor research can partly be due to differences in the time of determination. We conclude that conservation should focus on regional landscape management to mitigate risks on meta-populations from future, intensified extreme climate events. Reducing land use effects, thus improving patch quality and network connectivity, can compensate for effects of additional discrete stressors and, in turn, synergistic interactions.

Forest Management under Climate Change: A Decision Analysis of Thinning Interventions for Water Services and Biomass in a Norway Spruce Stand in South Germany

Climate change is producing threats to forests’ capacity of regulating water regimes. Therefore, thinning strategies can be applied to mitigate climate change impacts more efficiently by providing more spaces for trees to utilize resources e.g., water and nutrients. This study examined the effects of different thinning intensities and intervals on water characteristics and biomass growth of a 75-year-old Norway spruce (Picea abies) stand in the Black Forest, Germany. Here we used a water and management sensitive update of the process-based forest growth model 3PG, 3PG-Hydro. We applied light (10%), moderate (30%), and heavy thinning (50% intensity) in the interval of 10, 25, and 50 years of the management period. We simulated growth with climate change scenario RCP 8.5 data from 1995 to 2065. We analyzed the effects of the different thinning regimens on biomass, evapotranspiration as well as water yield. Thinning intensity and interval as well as their interaction have significant influence on production of stand biomass and water yield for all thinning regimes applied (p < 0.05). However, there is no significant difference (p > 0.05) in accumulated biomass (thinned biomass added to the stand biomass) between the applied thinning regimes. Light thinning in a long interval (50 years) produced highest stand biomass among the applied thinning regimes. Furthermore, the prediction showed that accumulated water yield increased with increasing thinning intensity. Our study concludes that repeated moderate thinning at intermediate intervals results in a high water yield without losing biomass production.

Attributions of Evapotranspiration and Gross Primary Production Changes in Semi-Arid Region: A Case Study in the Water Source Area of the Xiong’an New Area in North China

Investigating the attributions of evapotranspiration (ET) and gross primary production (GPP) changes is of great importance for regional, sustainable water resources and ecological management in semi-arid regions. Based on the simulation conducted during 2000–2019 by improving water-carbon coupling Distributed Time Variant Gain Model, the trends of ET and GPP were estimated and the driving factors were identified via 10 experimental scenarios in the water source area of the Xiong’an New Area in North China. The results show significant increases both in ET and GPP by 2.4 mm/a and 6.0 gC/m2/a in the region, respectively. At the annual scale, increasing precipitation dominates the ET uptrend. Air temperature, humidity and the interactive effects also contribute to the ET uptrend, and the contributions are 12.8%, 2.0% and 2.3%, respectively, while elevated atmospheric CO2 concentration (eCO2) and solar dimming lead to ET changes of about −7.2% and −12.4%, respectively. For the GPP changes, the increase in GPP is mainly caused by eCO2, increasing precipitation and rising temperature with the contributions of 56.7%, 34.8% and 27.8%, respectively. Solar dimming, humidity and windspeed contribute −6.8%, −4.8% and −3.5% of the GPP changes. Compared to climate change, land use and cover change has smaller effects on both ET and GPP for the few changes in land coverage. At the seasonal scale, ET and GPP increase to a greater extent during the growing season in spring and summer than in autumn and winter. Precipitation, temperature and eCO2 are generally the main causes for ET and GPP changes. Meanwhile, the decreasing humidity and rising temperature are dominant factors for ET and GPP increases, respectively, in winter. Furthermore, solar dimming has strong effects on ET reduction in autumn. The contribution of the interactive effects is much higher on a seasonal scale than annual scale, contributing to considerable decreases in ET and GPP in spring, increases in ET in autumn and winter, and an increase in GPP in winter. This study highlights the importance of considering water-carbon coupling on the attributions of ET and GPP changes and the differentiation of the effects by the abovementioned influential factors at annual and seasonal scales.

Automatic variable selection in ecological niche modeling: A case study using Cassin’s Sparrow (Peucaea cassinii)

MERRA/Max provides a feature selection approach to dimensionality reduction that enables direct use of global climate model outputs in ecological niche modeling. The system accomplishes this reduction through a Monte Carlo optimization in which many independent MaxEnt runs, operating on a species occurrence file and a small set of randomly selected variables in a large collection of variables, converge on an estimate of the top contributing predictors in the larger collection. These top predictors can be viewed as potential candidates in the variable selection step of the ecological niche modeling process. MERRA/Max’s Monte Carlo algorithm operates on files stored in the underlying filesystem, making it scalable to large data sets. Its software components can run as parallel processes in a high-performance cloud computing environment to yield near real-time performance. In tests using Cassin’s Sparrow (Peucaea cassinii) as the target species, MERRA/Max selected a set of predictors from Worldclim’s Bioclim collection of 19 environmental variables that have been shown to be important determinants of the species’ bioclimatic niche. It also selected biologically and ecologically plausible predictors from a more diverse set of 86 environmental variables derived from NASA’s Modern-Era Retrospective Analysis for Research and Applications Version 2 (MERRA-2) reanalysis, an output product of the Goddard Earth Observing System Version 5 (GEOS-5) modeling system. We believe these results point to a technological approach that could expand the use global climate model outputs in ecological niche modeling, foster exploratory experimentation with otherwise difficult-to-use climate data sets, streamline the modeling process, and, eventually, enable automated bioclimatic modeling as a practical, readily accessible, low-cost, commercial cloud service.

Differential Equation Models in Applied Mathematics: Theoretical and Numerical Challenges

The articles published in the Special Issue “Differential Equation Models in Applied Mathematics: Theoretical and Numerical Challenges” of the MDPI Mathematics journal are here collected [...]

Noise-induced versus intrinsic oscillation in ecological systems

Studies of oscillatory populations have a long history in ecology. A first-principles understanding of these dynamics can provide insights into causes of population regulation and help with selecting detailed predictive models. A particularly difficult challenge is determining the relative role of deterministic versus stochastic forces in producing oscillations. We employ statistical physics concepts, including measures of spatial synchrony, that incorporate patterns at all scales and are novel to ecology, to show that spatial patterns can, under broad and well-defined circumstances, elucidate drivers of population dynamics. We find that when neighbours are coupled (e.g. by dispersal), noisy intrinsic oscillations become distinguishable from noise-induced oscillations at a transition point related to synchronisation that is distinct from the deterministic bifurcation point. We derive this transition point and show that it diverges from the deterministic bifurcation point as stochasticity increases. The concept of universality suggests that the results are robust and widely applicable.

Mapping climate change's impact on cholera infection risk in Bangladesh

Several studies have investigated how Vibrio cholerae infection risk changes with increased rainfall, temperature, and water pH levels for coastal Bangladesh, which experiences seasonal surges in cholera infections associated with heavy rainfall events. While coastal environmental conditions are understood to influence V. cholerae propagation within brackish waters and transmission to and within human populations, it remains unknown how changing climate regimes impact the risk for cholera infection throughout Bangladesh. To address this, we developed a random forest species distribution model to predict the occurrence probability of cholera incidence within Bangladesh for 2015 and 2050. We developed a random forest model trained on cholera incidence data and spatial environmental raster data to be predicted to environmental data for the year of training (2015) and 2050. From our model's predictions, we generated risk maps for cholera occurrence for 2015 and 2050. Our best-fitting model predicted cholera occurrence given elevation and distance to water. Generally, we find that regions within every district in Bangladesh experience an increase in infection risk from 2015 to 2050. We also find that although cells of high risk cluster along the coastline predominantly in 2015, by 2050 high-risk areas expand from the coast inland, conglomerating around surface waters across Bangladesh, reaching all but the northwestern-most district. Mapping the geographic distribution of cholera infections given projected environmental conditions provides a valuable tool for guiding proactive public health policy tailored to areas most at risk of future disease outbreaks.

Stability of tropical forest tree carbon-water relations in a rainfall exclusion treatment through shifts in effective water uptake depth

Increasing severity and frequency of drought is predicted for large portions of the terrestrial biosphere, with major impacts already documented in wet tropical forests. Using a 4-year rainfall exclusion experiment in the Daintree Rainforest in northeast Australia, we examined canopy tree responses to reduced precipitation and soil water availability by quantifying seasonal changes in plant hydraulic and carbon traits for 11 tree species between control and drought treatments. Even with reduced soil volumetric water content in the upper 1 m of soil in the drought treatment, we found no significant difference between treatments for predawn and midday leaf water potential, photosynthesis, stomatal conductance, foliar stable carbon isotope composition, leaf mass per area, turgor loss point, xylem vessel anatomy, or leaf and stem nonstructural carbohydrates. While empirical measurements of aboveground traits revealed homeostatic maintenance of plant water status and traits in response to reduced soil moisture, modeled belowground dynamics revealed that trees in the drought treatment shifted the depth from which water was acquired to deeper soil layers. These findings reveal that belowground acclimation of tree water uptake depth may buffer tropical rainforests from more severe droughts that may arise in future with climate change.

Making spatial-temporal marine ecosystem modelling better - A perspective

Marine Ecosystem Models (MEMs) provide a deeper understanding of marine ecosystem dynamics. The United Nations Decade of Ocean Science for Sustainable Development has highlighted the need to deploy these complex mechanistic spatial-temporal models to engage policy makers and society into dialogues towards sustainably managed oceans. From our shared perspective, MEMs remain underutilized because they still lack formal validation, calibration, and uncertainty quantifications that undermines their credibility and uptake in policy arenas. We explore why these shortcomings exist and how to enable the global modelling community to increase MEMs' usefulness. We identify a clear gap between proposed solutions to assess model skills, uncertainty, and confidence and their actual systematic deployment. We attribute this gap to an underlying factor that the ecosystem modelling literature largely ignores: technical issues. We conclude by proposing a conceptual solution that is cost-effective, scalable and simple, because complex spatial-temporal marine ecosystem modelling is already complicated enough.

Understanding and Modeling Forest Disturbance Interactions at the Landscape Level

Disturbances, both natural and anthropogenic, affect the configuration, composition, and function of forested ecosystems. Complex system behaviors emerge from the interactions between disturbance regimes, the vegetation response to those disturbances, and their interplay with multiple drivers (climate, topography, land use, etc.) across spatial and temporal scales. Here, we summarize conceptual advances and empirical approaches to disturbance interaction investigation, and used those insights to evaluate and categorize 146 landscape modeling studies emerging from a systematic review of the literature published since 2010. Recent conceptual advances include formal disaggregation of disturbances into their constituent components, embedding disturbance processes into system dynamics, and clarifying terminology for interaction factors, types, and ecosystem responses. Empirical studies investigating disturbance interactions now span a wide range of approaches, including (most recently) advanced statistical methods applied to an expanding set of spatial and temporal datasets. Concurrent development in spatially-explicit landscape models, informed by these empirical insights, integrate the interactions among natural and anthropogenic disturbances by coupling these processes to account for disturbance stochasticity, disturbance within and across scales, and non-linear landscape responses to climate change. Still, trade-offs between model elegance and complexity remain. We developed an index for the degree of process integration (i.e., balance of static vs. dynamic components) within a given disturbance agent and applied it to the studies from our systematic review. Contemporary model applications in this line of research have applied a wide range process integration, depending on the specific question, but also limited in part by data and knowledge. Non-linear “threshold” behavior and cross-scaled interactions remain a frontier in temperate, boreal, and alpine regions of North America and Europe, while even simplistic studies are lacking from other regions of the globe (e.g., subtropical and tropical biomes). Understanding and planning for uncertainty in system behavior—including disturbance interactions—is paramount at a time of accelerated anthropogenic change. While progress in landscape modeling studies in this area is evident, work remains to increase model transparency and confidence, especially for understudied regions and processes. Moving forward, a multi-dimensional approach is recommended to address the uncertainties of complex human-ecological dynamics.

Global process-based characterization factors of soil carbon depletion for life cycle impact assessment

Regionalization of land use (LU) impact in life cycle assessment (LCA) has gained relevance in recent years. Most regionalized models are statistical, using highly aggregated spatial units and LU classes (e.g. one unique LU class for cropland). Process-based modelling is a powerful characterization tool but so far has never been applied globally for all LU classes. Here, we propose a new set of spatially detailed characterization factors (CFs) for soil organic carbon (SOC) depletion. We used SOC dynamic curves and attainable SOC stocks from a process-based model for more than 17,000 world regions and 81 LU classes. Those classes include 63 agricultural (depending on 4 types of management/production), and 16 forest sub-classes, and 1 grassland and 1 urban class. We matched the CFs to LU elementary flows used by LCA databases at country-level. Results show that CFs are highly dependent on the LU sub-class and management practices. For example, transformation into cropland in general leads to the highest SOC depletion but SOC gains are possible with specific crops.

The Impact of Climate Change on Agricultural Insect Pests

Climate change and global warming are of great concern to agriculture worldwide and are among the most discussed issues in today's society. Climate parameters such as increased temperatures, rising atmospheric CO2 levels, and changing precipitation patterns have significant impacts on agricultural production and on agricultural insect pests. Changes in climate can affect insect pests in several ways. They can result in an expansion of their geographic distribution, increased survival during overwintering, increased number of generations, altered synchrony between plants and pests, altered interspecific interaction, increased risk of invasion by migratory pests, increased incidence of insect-transmitted plant diseases, and reduced effectiveness of biological control, especially natural enemies. As a result, there is a serious risk of crop economic losses, as well as a challenge to human food security. As a major driver of pest population dynamics, climate change will require adaptive management strategies to deal with the changing status of pests. Several priorities can be identified for future research on the effects of climatic changes on agricultural insect pests. These include modified integrated pest management tactics, monitoring climate and pest populations, and the use of modelling prediction tools.

A Decade's Change in Vegetation Productivity and Its Response to Climate Change over Northeast China

In this study, we simulated vegetation net primary productivity (NPP) using the boreal ecosystem productivity simulator (BEPS) between 2003 and 2012 over Northeast China, a region that is significantly affected by climate change. The NPP was then validated against the measurements that were calculated from tree ring data, with a determination coefficient (R²) = 0.84 and the root mean square error (RMSE) = 42.73 gC/m²·a. Overall, the NPP showed an increasing trend over Northeast China, with the average rate being 4.48 gC/m²·a. Subsequently, partial correlation and lag analysis were conducted between the NPP and climatic factors. The partial correlation analysis suggested that temperature was the predominant factor that accounted for changes in the forest NPP. Solar radiation was the main factor that affected the forest NPP, and the grass NPP was the most closely associated with precipitation. The relative humidity substantially affected the annual variability of the shrub and crop NPPs. The lag time of the NPP related to precipitation increased with the vegetation growth, and it was found that the lag period of the forest was longer than that of grass and crops, whereas the cumulative lag month of the forest was shorter. This comprehensive analysis of the response of the vegetation NPP to climate change can provide scientific references for the managing departments that oversee relevant resources.

Management implications of long transients in ecological systems

The underlying biological processes that govern many ecological systems can create very long periods of transient dynamics. It is often difficult or impossible to distinguish this transient behaviour from similar dynamics that would persist indefinitely. In some cases, a shift from the transient to the long-term, stable dynamics may occur in the absence of any exogenous forces. Recognizing the possibility that the state of an ecosystem may be less stable than it appears is crucial to the long-term success of management strategies in systems with long transient periods. Here we demonstrate the importance of considering the potential of transient system behaviour for management actions across a range of ecosystem organizational scales and natural system types. Developing mechanistic models that capture essential system dynamics will be crucial for promoting system resilience and avoiding system collapses.

Towards Building a Sustainable Future: Positioning Ecological Modelling for Impact in Ecosystems Management

As many ecosystems worldwide are in peril, efforts to manage them sustainably require scientific advice. While numerous researchers around the world use a great variety of models to understand ecological dynamics and their responses to disturbances, only a small fraction of these models are ever used to inform ecosystem management. There seems to be a perception that ecological models are not useful for management, even though mathematical models are indispensable in many other fields. We were curious about this mismatch, its roots, and potential ways to overcome it. We searched the literature on recommendations and best practices for how to make ecological models useful to the management of ecosystems and we searched for 'success stories' from the past. We selected and examined several cases where models were instrumental in ecosystem management. We documented their success and asked whether and to what extent they followed recommended best practices. We found that there is not a unique way to conduct a research project that is useful in management decisions. While research is more likely to have impact when conducted with many stakeholders involved and specific to a situation for which data are available, there are great examples of small groups or individuals conducting highly influential research even in the absence of detailed data. We put the question of modelling for ecosystem management into a socio-economic and national context and give our perspectives on how the discipline could move forward.

Nature-based solutions as enablers of circularity in water systems: A review on assessment methodologies, tools and indicators

Water has been pushed into a linear model, which is increasingly acknowledged of causing cumulative emissions of pollutants, waste stocks, and impacting on the irreversible deterioration of water and other resources. Moving towards a circular model in the water sector, the configuration of future water infrastructure changes through the integration of grey and green infrastructure, forming Nature-based Solutions (NBS) as an integral component that connects human-managed to nature-managed water systems. In this study, a thorough appraisal of the latest literature is conducted, providing an overview of the existing tools, methodologies and indicators that have been used to assess NBS for water management, as well as complete water systems considering the need of assessing both anthropogenic and natural elements. Furthermore, facilitators and barriers with respect to existing policies and regulations on NBS and circularity have been identified. The study concludes that the co-benefits of NBS for water management are not adequately assessed. A holistic methodology assessing complete water systems from a circularity perspective is still needed integrating existing tools (i.e. hydro-biogeochemical models), methods (i.e. MFA-based and LCA) and incorporating existing and/or newly-developed indicators.

Modeling the Effects of Global Change on Ecosystem Processes in a Tropical Rainforest

Research Highlights: Ongoing land-use change and climate change in wet tropical forests can potentially drive shifts in tree species composition, representing a change in individual species within a functional group, tropical evergreen trees. The impacts on the global carbon cycle are potentially large, but unclear. We explored the differential effects of species within this functional group, in comparison with the effects of climate change, using the Century model as a research tool. Simulating effects of individual tree species on biome-level biogeochemical cycles constituted a novel application for Century. Background and Objectives: A unique, long-term, replicated field experiment containing five evergreen tree species in monodominant stands under similar environmental conditions in a Costa Rican wet forest provided data for model evaluation. Our objectives were to gain insights about this forest’s biogeochemical cycles and effects of tree species within this functional group, in comparison with climate change. Materials and Methods: We calibrated Century, using long-term meteorological, soil, and plant data from the field-based experiment. In modeling experiments, we evaluated effects on forest biogeochemistry of eight plant traits that were both observed and modeled. Climate-change simulation experiments represented two climate-change aspects observed in this region. Results: Model calibration revealed that unmodeled soil processes would be required to sustain observed P budgets. In species-traits experiments, three separate plant traits (leaf death rate, leaf C:N, and allocation to fine roots) resulted in modeled biomass C stock changes of >50%, compared with a maximum 21% change in the climate-change experiments. Conclusions: Modeled ecosystem properties and processes in Century were sensitive to changes in plant traits and nutrient limitations to productivity. Realistic model output was attainable for some species, but unusual plant traits thwarted predictions for one species. Including more plant traits and soil processes could increase realism, but less-complex models provide an accessible means for exploring plant-soil-atmosphere interactions.

Hidden similarities in the dynamics of a weakly synchronous marine metapopulation

Populations of many marine species are only weakly synchronous, despite coupling through larval dispersal and exposure to synchronous environmental drivers. Although this is often attributed to observation noise, factors including local environmental differences, spatially variable dynamics, and chaos might also reduce or eliminate metapopulation synchrony. To differentiate spatially variable dynamics from similar dynamics driven by spatially variable environments, we applied hierarchical delay embedding. A unique output of this approach, the "dynamic correlation," quantifies similarity in intrinsic dynamics of populations, independently of whether their abundance is correlated through time. We applied these methods to 17 populations of blue crab (Callinectes sapidus) along the US Atlantic coast and found that their intrinsic dynamics were broadly similar despite largely independent fluctuations in abundance. The weight of evidence suggests that the latitudinal gradient in temperature, filtered through a unimodal response curve, is sufficient to decouple crab populations. As unimodal thermal performance is ubiquitous in ectotherms, we suggest that this may be a general explanation for the weak synchrony observed at large distances in many marine species, although additional studies are needed to test this hypothesis.

A Generically Parameterized model of Lake eutrophication (GPLake) that links field-, lab- and model-based knowledge

Worldwide, eutrophication is threatening lake ecosystems. To support lake management numerous eutrophication models have been developed. Diverse research questions in a wide range of lake ecosystems are addressed by these models. The established models are based on three key approaches: the empirical approach that employs field surveys, the theoretical approach in which models based on first principles are tested against lab experiments, and the process-based approach that uses parameters and functions representing detailed biogeochemical processes. These approaches have led to an accumulation of field-, lab- and model-based knowledge, respectively. Linking these sources of knowledge would benefit lake management by exploiting complementary information; however, the development of a simple tool that links these approaches was hampered by their large differences in scale and complexity. Here we propose a Generically Parameterized Lake eutrophication model (GPLake) that links field-, lab- and model-based knowledge and can be used to make a first diagnosis of lake water quality. We derived GPLake from consumer-resource theory by the principle that lacustrine phytoplankton is typically limited by two resources: nutrients and light. These limitations are captured in two generic parameters that shape the nutrient to chlorophyll-a relations. Next, we parameterized GPLake, using knowledge from empirical, theoretical, and process-based approaches. GPLake generic parameters were found to scale in a comparable manner across data sources. Finally, we show that GPLake can be applied as a simple tool that provides lake managers with a first diagnosis of the limiting factor and lake water quality, using only the parameters for lake depth, residence time and current nutrient loading. With this first-order assessment, lake managers can easily assess measures such as reducing nutrient load, decreasing residence time or changing depth before spending money on field-, lab- or model- experiments to support lake management.

Assessing the provision of carbon-related ecosystem services across a range of temperate grassland systems in western Canada

Reliable data on the provision of ecosystem services (ES) is essential to the design and implementation of policies that incorporate ES into grassland conservation and restoration. We developed and applied an innovative approach for regional parameterization, and calibration of the CENTURY ecosystem model. We quantified spatiotemporal variation of soil organic carbon stock (SOC) and aboveground plant biomass production (AGB) and examined their responses to the recent climate change across a diverse range of native grassland systems in Alberta, western Canada. The simultaneous integration of SOC and AGB into calibration and analysis accounted for most of the spatiotemporal variability in the SOC and AGB measurements and resulted in reduced simulation uncertainty across nine grassland regions. These findings suggest the importance of the systematic parameterization and calibration for the reliable assessment of carbon-related ES across a wide geographic area with heterogeneous ecological conditions. Simulation results showed a pronounced variation in the spatial distribution of SOC and AGB and their associated uncertainty across grassland regions. Under baseline grazing intensity regime, an overall negative effect of recent climatic changes on the SOC, and a less consistent effect on the AGB were found. While, an overall positive or slightly negative impact of recent climate change on the SOC and AGB was found under a proposed 10% lower grazing intensity regime. These heterogeneities in the magnitude and direction of climate change effects under different grazing regimes suggest needs for a range of climate change adaptation strategies to maintain carbon-related ES in Alberta's grasslands. The modeling framework developed in this study can be used to improve the spatially explicit assessment of carbon-related ES in other geographically vast grassland areas and examine the effectiveness of alternative management scenarios to ensure the long-term provision of carbon-related ES in grassland systems.

Modeling ammonia volatilization following the application of synthetic fertilizers to cultivated uplands with calcareous soils using an improved DNDC biogeochemistry model

Simulation of ammonia (NH₃) volatilization by process-oriented biogeochemical models, such as the widely used DeNitrification DeComposition (DNDC), is an imperative need to identify the best management strategies that can improve nitrogen use efficiency in crop production while alleviating environmental pollution. However, scarce validation has been impeding the applicability of the DNDC for this purpose. Using the micrometeorological or wind tunnel-based observations of NH₃ volatilization in 44 cases with at seven nationwide field sites in China, which were cultivated with summer maize and winter wheat in calcareous soils and applied with synthetic fertilizers, the DNDC was tested, modified, and evaluated in this study. The following major modifications were made in the model source codes. Primarily, pedo-transfer functions were introduced into the model to provide three soil hydraulic parameters that are required to simulate soil moisture. Then, the temperature effect on ammonium bicarbonate decomposition, which was originally missing, was parameterized. Finally, the effect of soil texture on ammonia volatilization from the liquid phase was re-parameterized while an adaption factor was set. Seven typical cases were involved in the model modifications and the other 37 independent cases were used for the modified model evaluation. Compared to the original model, the modified DNDC performed better. For instance, it showed a higher index of agreement of 0.77 versus 0.38, a higher modeling efficiency (Nash-Sutcliffe index) of 0.19 versus -0.52, and a greater determination coefficient (R²) of 0.35 (p < 0.001) versus no available value (i.e., R² ≤ 0) in the zero-intercept linear regression of the observed cumulative NH₃ volatilizations during individual measurement periods against the simulations. Future studies are needed to further improve the modified DNDC so as to better simulate the effects of rainfall/irrigation and deep placement of fertilizers on NH₃ volatilization from calcareous soils cultivated with upland crops.