How to make ecological models useful for environmental management

Insights into the linkages of forest structure dynamics with ecosystem services

Large-scale land cover changes leading to land degradation and deforestation in fragile ecosystems such as the Western Ghats have impaired ecosystem services, evident from the conversion of perennial water bodies to seasonal, which necessitates an understanding of forest structure dynamics with ecosystem services to evolve appropriate location-specific mitigation measures to arrest land degradation. The current study evaluates the extent and condition of forest ecosystems in Goa of the Central Western Ghats, a biodiversity hotspot. Land use dynamics is assessed through a supervised hierarchical classifier based on the Random Forest Machine Learning Algorithm, revealing that total forest cover declined by 3.75% during the post-1990s due to market forces associated with globalization. Likely land uses predicated through the CA-Markov-based Analytic Hierarchy Process (AHP) highlight a decline in evergreen forest cover of 10.98%. The carbon sequestration potential of forests in Goa assessed through the InVEST model highlights the storage of 56,131.16 Gg of carbon, which accounts for 373.47 billion INR (4.49 billion USD). The total ecosystem supply value (TESV) for forest ecosystems was computed by aggregating the provisioning, regulating, and cultural services, which accounts for 481.76 billion INR per year. TESV helps in accounting for the degradation cost of ecosystems towards the development of green GDP (Gross Domestic Product). Prioritization of Ecologically Sensitive Regions (ESR) considering bio-geo-climatic, ecological, and social characteristics at disaggregated levels reveals that 54.41% of the region is highly sensitive (ESR1 and ESR2). The outcome of the research offers invaluable insights for the formulation of strategic natural resource management approaches.

Integration of temperature-driven population model and pest monitoring data to estimate initial conditions and timing of first field invasion: application to the cassava whitefly, Bemisia tabaci

Empirical field data and simulation models are often used separately to monitor and analyse the dynamics of insect pest populations over time. Greater insight may be achieved when field data are used directly to parametrize population dynamic models. In this paper, we use a differential evolution algorithm to integrate mechanistic physiological-based population models and monitoring data to estimate the population density and the physiological age of the first cohort at the start of the field monitoring. We introduce an ad hoc temperature-driven life-cycle model of Bemisia tabaci in conjunction with field monitoring data. The likely date of local whitefly invasion is estimated, with a subsequent improvement of the model's predictive accuracy. The method allows computation of the likely date of the first field incursion by the pest and demonstrates that the initial physiological age somewhat neglected in prior studies can improve the accuracy of model simulations. Given the increasing availability of monitoring data and models describing terrestrial arthropods, the integration of monitoring data and simulation models to improve model prediction and pioneer invasion date estimate will lead to better decision-making in pest management.

Quantifying the relationship between observed variables with censored values using Bayesian error-in-variables regression

We aimed to address two common challenges for scientists working with observational data: "how to quantify the relationship between two observed (or measured) variables", and, "how to account for censored values" (i.e., observations or measures whose value is only known to fall within a range). Quantifying the relationship between observed variables, and predicting one variable from the other (and vice versa), violates the assumption of standard regression regarding the existence of an independent, explanatory variable that is observed with no (or limited) uncertainty. To overcome this challenge, we developed and tested a Bayesian error-in-variables, EIV, regression model which accounts for uncertainty in variables orthogonally. Moreover, parameter estimation using Bayesian inference allowed the full parameter uncertainty to be propagated into probabilistic model predictions suitable for decision making. Alternative model formulations were applied to a dataset containing measured concentrations of organic pollutants in mothers and their eggs from the freshwater turtle Malaclemys terrapin and validated against an independent dataset of the turtle Chelydra serpentina. The best performing EIV model was then applied to the dataset again after censoring measurements in one or both variables. Here, independent likelihoods for both censored and uncensored data were formulated and then easily combined following the Bayesian implementation of the model. The EIV model performed well, as revealed by posterior predictive checks around 85%, and obtained comparable parameter estimates in both censored and uncensored cases. The resulting model allows scientists and decision-makers to quantitatively link variables, and make predictions from one variable to the next while accounting for uncertainties and censored data.

Applied Solutions to Balance Conservation Need With Practical Applications: A Case Study With Eagles Movement Models and Wind Energy Development

The wind energy industry presents a green-green dilemma whereby it aims to reduce CO2 emissions and combat climate change, benefiting biodiversity, but its development also negatively impacts biodiversity. To reconcile this, the first action in the mitigation hierarchy is to avoid development in high-risk areas for vulnerable species. For raptors, development is often restricted within a certain distance from nests, or more recently, by using predictive habitat use models to define site- and species-specific areas of high collision risk. One such model has been used to predict areas of high collision risk where development should be avoided for Verreaux's Eagles (Aquila verreauxii) in South Africa, but industry use of this tool has declined (research-implementation gap, RIG). Uncertainty over the model outputs is a likely cause of the RIG because the model results in variably sized exclusion areas for each development. To reduce this uncertainty and increase implementation of the model, we explore if limiting these predicted risk areas to protect the same amount of space or less, as a circular buffer around the nest, provides improved protection for the species. We found that by fixing the area of risk to be equal to the area of the current circular buffer recommendation, eagle protection, that is, the proportion of space used by eagles that is protected, was improved by around 6%-7% compared to circular buffers or by 2%-3% compared to previous threshold-based classifications. This fixed-area approach ensures that by applying the collision risk potential model there is no unexpected loss in developable area for wind energy developers. Our study demonstrates the importance of understanding and adapting tools that aim to promote sustainable development of renewable energy. Responding to stakeholder needs and balancing conservation with practical applications is critical, particularly in countries where policy enforcement is lacking.

Evaluation process for matching population models to regulatory decisions regarding threatened or endangered species by considering model risk

Population models can be an important tool in regulatory decision-making processes regarding natural resources, such as fisheries and rare species. Regulators presented with population models for their use often do not have the specific expertise to gauge the appropriateness of the model to their specific regulatory situation and decline their use in an abundance of caution. In other cases, regulators want to be involved with model development but may lack confidence in the utility of the models and their contribution to model development. The proposed process aims to address these concerns about using population models. The utility of population models depends on the available species data and the alignment of the model structure with regulatory needs. Importantly, the confidence in the available data and the model rigor need to match the types of decisions to be made, the time frame for reassessment, and the level of risk the regulator/agency deems appropriate. Model risk, defined as the possibility that the model is wrong or the output is misapplied, may stem from data limitations, parameter estimation uncertainty, model misspecification, or inappropriate use of a model. Here, we recommend a decision framework for considering the use of population models as a line of evidence in various regulatory contexts. The framework will assist regulators as they either work with modelers to construct new models or as they select from existing models to inform their decisions. Acknowledging and managing model risk increases the confidence of using models in regulatory contexts. As we move forward with utilizing models in regulatory decision-making, use of this process will ensure models fit the regulatory question, reduce model risk, and increase user confidence in applying models.

Identification of Ecological Hotspots Using the Eco-track: Case of Keoladeo National Park, Bharatpur, India

Ecological conservation and sustainable land management are vital endeavors in the face of rising anthropogenic pressures and habitat deterioration. Accurate and effective evaluation techniques are essential for identifying regions that are of ecological relevance and concern. The present research introduces an innovative approach using geospatial tools to detect natural hotspots and deficits within a landscape. The research incorporates six essential ecological parameters, namely spatial variability, vegetation health, road network connectivity, fragmentation, biological richness, and habitat areas, obtained from existing literature studies. These parameters indicate the overall health of ecosystems and the extent of biodiversity present, which are crucial for developing effective strategies for ecological planning. The research project aims to use geospatial applications to identify the "ecological rich", "ecological moderate" or "ecological deficit" areas in the study area and to establish a model framework for automating the geospatial analysis. The resulting map offers a comprehensive and practical depiction of the ecological condition of the landscape, facilitating decision-makers in strategically allocating resources for conservation and restoration initiatives. The importance of this research resides in its capacity to streamline and automate what was previously a time-consuming and labor-intensive procedure. This innovative approach empowers conservationists, land managers, and policymakers with a powerful tool "Eco-track' to identify and prioritize ecological hotspots and deficits, ultimately fostering more effective and targeted efforts in preserving the natural heritage.

Stressor fluctuations alter mechanisms underpinning seagrass responses to multiple stressors

Multiple anthropogenic stressors degrade ecosystems globally. A key knowledge gap in multiple stressor research is how variability in stressor intensity (i.e., fluctuations) and synchronicity (i.e., timing of fluctuations) affect biological responses, and the mechanisms underpinning responses. We evaluated the mechanistic effects of reduced light and herbicide contamination on seagrass, and determined how variations in stressor intensity and synchronicity influence the underlying mechanisms of responses. We used structural causal modelling and structural equation modelling to elucidate direct and mediating effects. Out-of-phase introduction (i.e., asynchronous fluctuations) altered the mechanistic pathways of how stressors affect seagrass relative to static stressors, and resulted in the greatest biomass loss (under the most intense stressor combination, ∼50 % reduction). Therefore, previous experiments that predominantly test only static stressor intensities might underestimate detrimental impacts in nature. Future experiments should explore mechanistic effects across realistic stressor intensities and synchronicities to improve our understanding and management of multiple stressors.

Assessing changing baleen whale distributions and reported incidents relative to vessel activity in the Northwest Atlantic

Baleen whales are among the largest marine megafauna, and while mostly well-protected from direct exploitation, they are increasingly affected by vessel traffic, interactions with fisheries, and climate change. Adverse interactions, notably vessel strikes and fishing gear entanglement, often result in distress, injury, or death for these animals. In Atlantic Canadian waters, such negative interactions or 'incidents' are consistently reported to marine animal response organizations but have not yet been analyzed relative to the spatial distribution of whales and vessels. Using a database of 483,003 whale sightings, 1,110 incident reports, and 82 million hours of maritime vessel activity, we conducted a spatiotemporal vulnerability analysis for all six baleen whale species occurring in the Northwest Atlantic Ocean by developing an ensemble of habitat-suitability models. The relative spatial risk of vessel-induced incidents was assessed for present (1985-2015) and projected near-future (2035-2055) distributions of baleen whales. Areas of high habitat suitability for multiple baleen whale species were intrinsically linked to sea surface temperature and salinity, with multispecies hotspots identified in the Bay of Fundy, Scotian Shelf, Laurentian Channel, Flemish Cap, and Gulf of St. Lawrence. Present-day model projections were independently evaluated using a separate database of acoustic detections and found to align well. Regions of high relative incident risk were projected close to densely inhabited regions, principal maritime routes, and major fishing grounds, in general coinciding with reported incident hotspots. While some high-risk regions already benefit from mitigation strategies aimed at protecting North Atlantic Right Whales, our analysis highlights the importance of considering risks to multiple species, both in the present day and under continued environmental change.

Demographic effects of sanitary policies on European vulture population dynamics: A retrospective modeling approach

The prediction of population responses to environmental changes, including the effects of different management scenarios, is a useful tool and a necessary contributor to improving conservation decisions. Empirical datasets based on long-term monitoring studies are essential to assess the robustness of retrospective modeling predictions on biodiversity. These allow checks on the performance of modeling projections and enable improvements to be made to future models, based on the errors detected. Here, we assess the performance of our earlier model to assess the impact of vulture food shortages caused by sanitary regulations on the population dynamics of Spanish vultures during the past decade (2009-2019). This model forecasts the population trends of three vulture species (griffon, Egyptian, and bearded vultures) in Spain (home to 90% of the European vulture population) under various food shortage scenarios. We show that it underestimated bearded and griffon vulture population numbers and overestimated Egyptian vultures. The model suggested that the most plausible food shortage scenario involved an approximate 50% reduction of livestock carcass availability in the ecosystem compared with the previous situation without sanitary carcass removal. However, the observed annual population growth for the period 2009-2019 (7.8% for griffon vulture, 2.4% for Egyptian vulture, and 3.5% for bearded vulture) showed that food shortages had little impact on vulture population dynamics. After assessing the robustness of the model, we developed a new model with updated demographic parameters and foraging movements under different hypothetical food shortage scenarios for the period 2019-2029. This model forecasts annual population increases of about 3.6% for the bearded vulture, 3.7% for the Egyptian vulture, and 1.1% for the Griffon vulture. Our findings suggest that food shortages due to the implementation of sanitary policies resulted in only a moderate impact on vulture population growth, probably thanks to the supplementary feeding network which provided alternative food. Also important was the availability of alternative food sources (intensive farms, landfills) that were used more regularly than expected. We discuss the computational performance of our modeling approach and its management consequences to improve future conservation measures for these threatened species, which provide essential ecosystem services.

Creating simple predictive models in ecology, conservation and environmental policy based on Bayesian belief networks

Predictive models are often complex to produce and interpret, yet can offer valuable insights for management, conservation and policy-making. Here we introduce a new modelling tool (the R package 'BBNet'), which is simple to use, and requires little mathematical or computer programming background. By using straightforward concepts to describe interactions between model components, predictive models can be effectively constructed using basic spreadsheet tools and loaded into the R package. These models can be analysed, visualised, and sensitivity tested to assess how information flows through the system's components and provide predictions for future outcomes of the systems. This paper provides a theoretical background to the models, which are modified Bayesian belief networks (BBNs), and an overview of how the package can be used. The models are not fully quantitative, but outcomes between different modelled scenarios can be considered ordinally (i.e. ranked from 'best' to 'worse'). Parameterisation of models can also be through data, literature, expert opinion, questionnaires and/or surveys of opinion, which are expressed as a simple 'weak' to 'very strong' or 1-4 integer value for interactions between model components. While we have focussed on the use of the models in environmental and ecological problems (including with links to management and social outcomes), their application does not need to be restricted to these disciplines, and use in financial systems, molecular biology, political sciences and many other disciplines are possible.

Transferability of a Bayesian Belief Network across diverse agricultural catchments using high-frequency hydrochemistry and land management data

Biogeochemical catchment models are often developed for a single catchment and, as a result, often generalize poorly beyond this. Evaluating their transferability is an important step in improving their predictive power and application range. We assess the transferability of a recently developed Bayesian Belief Network (BBN) that simulated monthly stream phosphorus (P) concentrations in a poorly-drained grassland catchment through application to three further catchments with different hydrological regimes and agricultural land uses. In all catchments, flow and turbidity were measured sub-hourly from 2009 to 2016 and supplemented with 400-500 soil P test measurements. In addition to a previously parameterized BBN, five further model structures were implemented to incorporate in a stepwise way: in-stream P removal using expert elicitation, additional groundwater P stores and delivery, and the presence or absence of septic tank treatment, and, in one case, Sewage Treatment Works. Model performance was tested through comparison of predicted and observed total reactive P (TRP) concentrations and percentage bias (PBIAS). The original BBN accurately simulated the absolute values of observed flow and TRP concentrations in the poorly and moderately drained catchments (albeit with poor apparent percentage bias scores; 76 % ≤ PBIAS≤94 %) irrespective of the dominant land use, but performed less well in the groundwater-dominated catchments. However, including groundwater total dissolved P (TDP) and Sewage Treatment Works (STWs) inputs, and in-stream P uptake improved model performance (-5 % ≤ PBIAS≤18 %). A sensitivity analysis identified redundant variables further helping to streamline the model applications. An enhanced BBN model capable for wider application and generalisation resulted.

Nanoplasmonic biosensors for environmental sustainability and human health

Monitoring the health conditions of the environment and humans is essential for ensuring human well-being, promoting global health, and achieving sustainability. Innovative biosensors are crucial in accurately monitoring health conditions, uncovering the hidden connections between the environment and human well-being, and understanding how environmental factors trigger autoimmune diseases, neurodegenerative diseases, and infectious diseases. This review evaluates the use of nanoplasmonic biosensors that can monitor environmental health and human diseases according to target analytes of different sizes and scales, providing valuable insights for preventive medicine. We begin by explaining the fundamental principles and mechanisms of nanoplasmonic biosensors. We investigate the potential of nanoplasmonic techniques for detecting various biological molecules, extracellular vesicles (EVs), pathogens, and cells. We also explore the possibility of wearable nanoplasmonic biosensors to monitor the physiological network and healthy connectivity of humans, animals, plants, and organisms. This review will guide the design of next-generation nanoplasmonic biosensors to advance sustainable global healthcare for humans, the environment, and the planet.

A deep learning classification framework for research methods of marine protected area management

The latest emerging transdisciplinary marine protected area (MPA) research scheme requires efficient approaches for theoretically based and data-driven method integration. However, due to the rapid development and diversification of research methods, it is growingly difficult to locate new methods in methodological dimensions and integrate them to the utmost utility. This study proposes a deep learning-based classification framework for MPA management methods focused particularly on data and theory capabilities using natural language processing (NLP). It extracted keywords from academic sources and performed clustering based on semantic similarity, generating benchmark texts for abstract labeling. By training the deep learning NLP model and analyzing the abstracts of 9049 MPA management empirical research articles from 1986 to 2024, the data and theory scores were attributed to each article, and a total of 19 major method categories and 110 segment branches were identified in qualitative, quantitative, and mixed genres. Combination types of research methods were summarized, yielding the data-theory neutralization principle where the average data and theory scores tend to approximate 0.50. Applying the principle broadens traditional boundaries for method integration and extends method synthesis to higher numbers, generating a practical research 2paradigm for future MPA research. Implications include bridging social and ecological data, theorizing emergent challenges in complex systems and integrating theory construction and data science. The framework is applicable to quantification of other environmental management disciplines and can serve as guidance for multidisciplinary method integration. © 2017 Elsevier Inc. All rights reserved.

Key challenges and developments in wildlife ecological risk assessment: Problem formulation

Problem formulation (PF) is a critical initial step in planning risk assessments for chemical exposures to wildlife, used either explicitly or implicitly in various jurisdictions to include registration of new pesticides, evaluation of new and existing chemicals released to the environment, and characterization of impact when chemical releases have occurred. Despite improvements in our understanding of the environment, ecology, and biological sciences, few risk assessments have used this information to enhance their value and predictive capabilities. In addition to advances in organism-level mechanisms and methods, there have been substantive developments that focus on population- and systems-level processes. Although most of the advances have been recognized as being state-of-the-science for two decades or more, there is scant evidence that they have been incorporated into wildlife risk assessment or risk assessment in general. In this article, we identify opportunities to consider elevating the relevance of wildlife risk assessments by focusing on elements of the PF stage of risk assessment, especially in the construction of conceptual models and selection of assessment endpoints that target population- and system-level endpoints. Doing so will remain consistent with four established steps of existing guidance: (1) establish clear protection goals early in the process; (2) consider how data collection using new methods will affect decisions, given all possibilities, and develop a decision plan a priori; (3) engage all relevant stakeholders in creating a robust, holistic conceptual model that incorporates plausible stressors that could affect the targets defined in the protection goals; and (4) embrace the need for iteration throughout the PF steps (recognizing that multiple passes may be required before agreeing on a feasible plan for the rest of the risk assessment). Integr Environ Assess Manag 2024;20:658-673. © 2022 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC). This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.

Data-centric species distribution modeling: Impacts of modeler decisions in a case study of invasive European frog-bit

Premise

Species distribution models (SDMs) are widely utilized to guide conservation decisions. The complexity of available data and SDM methodologies necessitates considerations of how data are chosen and processed for modeling to enhance model accuracy and support biological interpretations and ecological applications.

Methods

We built SDMs for the invasive aquatic plant European frog-bit using aggregated and field data that span multiple scales, data sources, and data types. We tested how model results were affected by five modeler decision points: the exclusion of (1) missing and (2) correlated data and the (3) scale (large-scale aggregated data or systematic field data), (4) source (specimens or observations), and (5) type (presence-background or presence-absence) of occurrence data.

Results

Decisions about the exclusion of missing and correlated data, as well as the scale and type of occurrence data, significantly affected metrics of model performance. The source and type of occurrence data led to differences in the importance of specific explanatory variables as drivers of species distribution and predicted probability of suitable habitat.

Discussion

Our findings relative to European frog-bit illustrate how specific data selection and processing decisions can influence the outcomes and interpretation of SDMs. Data-centric protocols that incorporate data exploration into model building can help ensure models are reproducible and can be accurately interpreted in light of biological questions.

Ecological threshold of phosphorus load in Baiyangdian Lake based on a PCLake model and ecological network analysis

Ecological thresholds are a useful indicator for implementing ecological management. Many studies determine the thresholds for nutrient loads in lakes based on the maximum allowable concentration of chlorophyll a (Chla), although this neglects the overall performance of the ecosystem. A PCLake model of Baiyangdian (BYD) Lake in northern China was constructed with six ecological network analysis (ENA) indicators that characterized the ecosystem function, system maturity, and food web structure to quantify the overall status of the BYD ecosystem. To my knowledge, this is the first study on the system level responses of the BYD Lake to phosphorus load interference. Different phosphorus load scenarios were designed to simulate the ecological responses of BYD Lake. The simulated results were employed to calculate the ENA indicators. Ecological thresholds were determined through the driving response relationship between the phosphorus load gradient and the ENA indicators. The results show a non-linear transition response of ENA indicator under phosphorus load gradient. As phosphorus load increases, D/H, SOI, and FCI decreases while A/DC, TPP/TR, and TPP/TB increases. This indicates that the overall structure and function of the ecosystem will deteriorate if phosphorus load increases. The phosphorus load thresholds for the overall performance of BYD Lake were 0.50-1.32 mg m-2 d-1, slightly wider than that of Chla (0.53-1.26 mg m-2 d-1). The model results clearly indicate that there is a time-lag phenomenon at the switch points in the response of ENA indicators compared to that of single functional group. In addition, the A/DC, TPP/TR, SOI, and FCI present more time-lag than that of other ENA indicators. These time-lag effects provide a particular opportunity for biodiversity conservation. Therefore, a possible management strategy is proposed to combine system-level and function group-level thresholds, with the ENA-based threshold as the bottom line and the phytoplankton's threshold as the early-warning indicator. This design is expected to be more precise and efficient, by exploiting the advantages of two thresholds, and may benefit for ecological management practices.

Bird species responses to rangeland management in relation to their traits: Rio de la Plata Grasslands as a case study

Areas used for livestock production and dominated by native grasses represent a unique opportunity to reconcile biodiversity conservation and livestock production. However, limited knowledge of individual species' responses to rangeland management restricts our capacity to design grazing practices that favor endangered species and other priority birds. In this work, we applied Hierarchical Modelling of Species Communities (HMSC) to study individual species responses, as well as the influence of traits on such responses, to variables related to rangeland management using birds of the Rio de la Plata Grasslands as a case study. Based on presence-absence data collected in 454 paddocks across 46 ranches we inferred the response of 69 species considering imperfect detection. This degree of detail fills a major gap in rangeland management, as species-level responses can be used to achieve targeted conservation goals other than maximizing richness or abundance. We found that artificial pastures had an overall negative impact on many bird species, whereas the presence of tussocks had a positive effect, including all threatened species. Grassland specialists were in general sensitive to grass height and tended to respond positively to tussocks but negatively to tree cover. Controlling grass height via adjustments in stocking rate can be a useful tool to favor grassland specialists. To favor a wide range of bird species in ranches, a mosaic of short and tall native grasslands with patches of tussocks and trees is desirable. We also found that species-specific responses were modulated by their traits: small-sized birds responded positively to tussocks and tree cover while large species responded negatively to increasing grass height. Ground foragers preferred short grass while birds that scarcely use this stratum were not affected by grass height. Results on the influence of traits on bird responses are an important novelty in relation to previous work in rangelands and potentially increase our predicting capacity and model transferability across grassland regions.

A Toxicokinetic-Toxicodynamic Modeling Workflow Assessing the Quality of Input Mortality Data

Toxicokinetic-toxicodynamic (TKTD) models simulate organismal uptake and elimination of a substance (TK) and its effects on the organism (TD). The Reduced General Unified Threshold model of Survival (GUTS-RED) is a TKTD modeling framework that is well established for aquatic risk assessment to simulate effects on survival. The TKTD models are applied in three steps: parameterization based on experimental data (calibration), comparing predictions with independent data (validation), and prediction of endpoints under environmental scenarios. Despite a clear understanding of the sensitivity of GUTS-RED predictions to the model parameters, the influence of the input data on the quality of GUTS-RED calibration and validation has not been systematically explored. We analyzed the performance of GUTS-RED calibration and validation based on a unique, comprehensive data set, covering different types of substances, exposure patterns, and aquatic animal species taxa that are regularly used for risk assessment of plant protection products. We developed a software code to automatically calibrate and validate GUTS-RED against survival measurements from 59 toxicity tests and to calculate selected model evaluation metrics. To assess whether specific survival data sets were better suited for calibration or validation, we applied a design in which all possible combinations of studies for the same species-substance combination are used for calibration and validation. We found that uncertainty of calibrated parameters was lower when the full range of effects (i.e., from high survival to high mortality) was covered by input data. Increasing the number of toxicity studies used for calibration further decreased parameter uncertainty. Including data from both acute and chronic studies as well as studies under pulsed and constant exposure in model calibrations improved model predictions on different types of validation data. Using our results, we derived a workflow, including recommendations for the sequence of modeling steps from the selection of input data to a final judgment on the suitability of GUTS-RED for the data set. Environ Toxicol Chem 2024;43:197-210. © 2023 Bayer AG and The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

A process framework for integrating stressor-response functions into cumulative effects models

Stressor-response (SR) functions quantify ecological responses to natural environmental variation or anthropogenic stressors. They are also core drivers of cumulative effects (CE) models, which are increasingly recognized as essential management tools to grapple with the diffuse footprint of human impacts. Here, we provide a process framework for the identification, development, and integration of SR functions into CE models, and highlight their consequential properties, behaviour, criteria for selecting appropriate stressors and responses, and general approaches for deriving them. Management objectives (and causal effect pathways) will determine the ultimate stressor and target response variables of interest (i.e., individual growth/survival, population size, community structure, ecosystem processes), but data availability will constrain whether proxies need to be used for the target stressor or response variables. Available data and confidence in underlying mechanisms will determine whether empirical or mechanistic (theoretical) SR functions are optimal. Uncertainty in underlying SR functions is often the primary source of error in CE modelling, and monitoring outcomes through adaptive management to iteratively refine parameterization of SR functions is a key element of model application. Dealing with stressor interactions is an additional challenge, and in the absence of known or suspected interaction mechanisms, controlling main effects should remain the primary focus. Indicators of suspected interaction presence (i.e., much larger or smaller responses to stressor reduction than expected during monitoring) should be confirmed through adaptive management cycles or targeted stressor manipulations. Where possible, management decisions should selectively take advantage of interactions to strategically mitigate stressor impacts (i.e., by using antagonisms to suppress stressor impacts, and by using synergisms to efficiently reduce them).

Mixing apples and oranges: Assessing ecological status and its confidence from multiple and diverse indicators

Ecosystem responses to increasing human pressures are complex and diverse, affecting organisms across all trophic levels. This has prompted the development of methods that integrate information across many indicators for environmental management. Legislative frameworks such as the European Water Framework Directive (WFD), specifically prescribe that integrated assessme nt (IA) of ecological status must consider indicators representing various biological and supporting quality elements. We present a general approach for an IA system based on a piece-wise linear transformation of indicator distributions to a standardized scale, allowing for integrating information from multiple and diverse indicators through a policy-dependent aggregation scheme. Uncertainties associated with monitoring data used for calculating indicators and their propagation throughout the integration scheme allow for confidence assessment at all levels of the hierarchical integration. Specific pressures leading to ecological impact can be identified through the most impaired indicators in the hierarchical and transparent aggregation scheme. The IA and its confidence are facilitated though the development of an online tool that accesses information from monitoring databases and presents the outcome at all levels of the assessment, ensuring consistency and transparency in the calculations for all potential stakeholders. We demonstrate the versality and applicability of the approach using indicators and aggregation principles from the Swedish national guidelines for assessing ecological status of rivers, lakes and coastal waters according to the WFD. Although the approach and the tool were developed specifically for the WFD ecological status assessment in Sweden, the generality of the approach implies that it can easily be adapted to the WFD assessment methods of other countries as well as other policies, where an integrated assessment is required.

Land Use and Land Cover Change Detection Using the Random Forest Approach: The Case of The Upper Blue Nile River Basin, Ethiopia

Monitoring land use change dynamics is critical for tackling food security, climate change, and biodiversity loss on a global scale. This study is designed to classify land use and land cover in the upper Blue Nile River Basin (BNRB) using a random forest (RF) algorithm. The Landsat images for Landsat 45, Landsat 7, and Landsat 8 are used for classification purposes. The study area is classified into seven land use/land cover classes: cultivated lands, bare lands, built-ups, forests, grazing lands, shrublands, and waterbodies. The accuracy of classified images is 83%, 85%, and 91% using the Kappa index of agreements. From 1983 to 2022 periods, cultivated lands and built-up areas increased by 47541 and 1777 km2, respectively, at the expense of grazing lands, shrublands, and forests. Furthermore, the area of water bodies has increased by 662 km2 due to the construction of small and large-scale irrigation and hydroelectric power generation dams. The main factors that determine agricultural land expansion are related to population growth. Therefore, land use and land cover change detection using a random forest is an important technique for multispectral satellite data classification to understand the optimal use of natural resources, conservation practices, and decision-making for sustainable development.

Estimating co-extinction threats in terrestrial ecosystems

The biosphere is changing rapidly due to human endeavour. Because ecological communities underlie networks of interacting species, changes that directly affect some species can have indirect effects on others. Accurate tools to predict these direct and indirect effects are therefore required to guide conservation strategies. However, most extinction-risk studies only consider the direct effects of global change-such as predicting which species will breach their thermal limits under different warming scenarios-with predictions of trophic cascades and co-extinction risks remaining mostly speculative. To predict the potential indirect effects of primary extinctions, data describing community interactions and network modelling can estimate how extinctions cascade through communities. While theoretical studies have demonstrated the usefulness of models in predicting how communities react to threats like climate change, few have applied such methods to real-world communities. This gap partly reflects challenges in constructing trophic network models of real-world food webs, highlighting the need to develop approaches for quantifying co-extinction risk more accurately. We propose a framework for constructing ecological network models representing real-world food webs in terrestrial ecosystems and subjecting these models to co-extinction scenarios triggered by probable future environmental perturbations. Adopting our framework will improve estimates of how environmental perturbations affect whole ecological communities. Identifying species at risk of co-extinction (or those that might trigger co-extinctions) will also guide conservation interventions aiming to reduce the probability of co-extinction cascades and additional species losses.

Aquatic environmental DNA: A review of the macro-organismal biomonitoring revolution

Environmental DNA (eDNA) is the fastest growing biomonitoring tool fuelled by two key features: time efficiency and sensitivity. Technological advancements allow rapid biodiversity detection at both species and community levels with increasing accuracy. Concurrently, there has been a global demand to standardise eDNA methods, but this is only possible with an in-depth overview of the technological advancements and a discussion of the pros and cons of available methods. We therefore conducted a systematic literature review of 407 peer-reviewed papers on aquatic eDNA published between 2012 and 2021. We observed a gradual increase in the annual number of publications from four (2012) to 28 (2018), followed by a rapid growth to 124 publications in 2021. This was mirrored by a tremendous diversification of methods in all aspects of the eDNA workflow. For example, in 2012 only freezing was applied to preserve filter samples, whereas we recorded 12 different preservation methods in the 2021 literature. Despite an ongoing standardisation debate in the eDNA community, the field is seemingly moving fast in the opposite direction and we discuss the reasons and implications. Moreover, by compiling the largest PCR-primer database to date, we provide information on 522 and 141 published species-specific and metabarcoding primers targeting a wide range of aquatic organisms. This works as a user-friendly 'distillation' of primer information that was hitherto scattered across hundreds of papers, but the list also reflects which taxa are commonly studied with eDNA technology in aquatic environments such as fish and amphibians, and reveals that groups such as corals, plankton and algae are under-studied. Efforts to improve sampling and extraction methods, primer specificity and reference databases are crucial to capture these ecologically important taxa in future eDNA biomonitoring surveys. In a rapidly diversifying field, this review synthetises aquatic eDNA procedures and can guide eDNA users towards best practice.

Science for social licence to arrest an ecosystem-transforming invasion

The primary role for scientific information in addressing complex environmental problems, such as biological invasions, is generally assumed to be as a guide for management decisions. However, scientific information often plays a minor role in decision-making, with practitioners instead relying on professional experience and local knowledge. We explore alternative pathways by which scientific information could help reduce the spread and impacts of invasive species. Our study centred on attempts to understand the main motivations and constraints of three local governance bodies responsible for the management of invasive (wilding) conifer species in the southern South Island of New Zealand in achieving strategic and operational goals. We used a combination of workshop discussions, questionnaire responses and visits to field sites to elicit feedback from study participants. We applied a mixed inductive-deductive thematic analysis approach to derive themes from the feedback received. The three main themes identified were: (1) impacts of wilding conifers and goals for wilding conifer control, (2) barriers to achieving medium- and long-term goals, and (3) science needed to support wilding conifer control. Participants identified reversal and prevention of both instrumental (e.g. reduced water availability for agriculture) and intrinsic (e.g. loss of biodiversity and landscape values) impacts of wilding conifer invasions as primary motivators behind wilding conifer control. Barriers to achieving goals were overwhelmingly social, relating either to unwillingness of landowners to participate or poorly designed regulatory frameworks. Consequently, science needs related primarily to gaining social licence to remove wilding conifers from private land and for more appropriate regulations. Scientific information provided via spread and impacts forecasting models was viewed as a key source of scientific information in gaining social licence. International experience suggests that invasive species control programmes often face significant external social barriers. Thus, for many biological invasions, the primary role of science might be to achieve social licence and regulatory support for the long-term goals of invasive species control programmes and the management interventions required to achieve those goals.

Distinct indicators of land use and hydrology characterize different aspects of riverine phytoplankton communities

Given the many threats to freshwater biodiversity, we need to be able to resolve which of the multiple stressors present in rivers are most important in driving change. Phytoplankton are a key component of the aquatic ecosystem, their abundance, species richness and functional richness are important indicators of ecosystem health. In this study, spatial variables, physiochemical conditions, water flow alterations and land use patterns were considered as the joint stressors from a lowland rural catchment. A modeling approach combining an ecohydrological model with machine learning was applied. The results implied that land use and flow regime, rather than nutrients, were most important in explaining differences in the phytoplankton community. In particular, the percentage of water body area and medium level residential urban area were key to driving the rising phytoplankton abundance in this rural catchment. The proportion of forest and pasture area were the leading factors controlling the variations of species richness. In this case deciduous forest cover affected the species richness in a positive way, while, pasture share had a negative effect. Indicators of hydrological alteration were found to be the best predictors for the differences in functional richness. This integrated model framework was found to be suitable for analysis of complex environmental conditions in river basin management. A key message would be the significance of forest area preservation and ecohydrological restoration in maintaining both phytoplankton richness and their functional role in river ecosystems.

Interactive effects of multiple stressors vary with consumer interactions, stressor dynamics and magnitude

Predicting the impacts of multiple stressors is important for informing ecosystem management but is impeded by a lack of a general framework for predicting whether stressors interact synergistically, additively or antagonistically. Here, we use process-based models to study how interactions generalise across three levels of biological organisation (physiological, population and consumer-resource) for a two-stressor experiment on a seagrass model system. We found that the same underlying processes could result in synergistic, additive or antagonistic interactions, with interaction type depending on initial conditions, experiment duration, stressor dynamics and consumer presence. Our results help explain why meta-analyses of multiple stressor experimental results have struggled to identify predictors of consistently non-additive interactions in the natural environment. Experiments run over extended temporal scales, with treatments across gradients of stressor magnitude, are needed to identify the processes that underpin how stressors interact and provide useful predictions to management.

Sensitivity mapping informs mitigation of bird mortality by collision with high-voltage power lines

Mapping the relative risk of impact on nature by a human infrastructure at a landscape scale (“sensitivity mapping”) is an essential tool for minimising the future impact of new development or for prioritising mitigation of existing impacts. High-voltage power lines (“transmission lines”) are known to increase bird mortality by collision. Here we present a method to derive a high resolution map of relative risk of transmission line impacts across one entire country, Belgium, from existing bird distribution data. First, all the bird species observed in Belgium were systematically assessed using literature and casualty records to select those to be included in the sensitivity map. Species were selected on the basis of their intrinsic susceptibility to collision and the conservation relevance of avoiding additional mortality for that species in Belgium. Each of the selected species was included in one or several spatial layer constructed from existing data, emerging from citizen science bird monitoring schemes. The resulting 17 layers were then combined into one final sensitivity map, where a “risk score” estimates the relative collision risk across Belgium at a 1×1 km resolution. This risk score is relatively robust to the subtraction of any of the 17 layers. The map identifies areas where building new transmission lines would create high risk of collision and, if overlapped with existing power lines, helps to prioritise spans where mitigation measures should be placed. Wetlands and river valleys stand out as the most potentially dangerous areas for collision with transmission lines. This sensitivity map could be regularly updated with new bird data or adapted to other countries where similar bird data are available.

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.

Capturing the dynamics of small populations: A retrospective assessment using long-term data for an island reintroduction

The art of population modelling is to incorporate factors essential for capturing a population's dynamics while otherwise keeping the model as simple as possible. However, it is unclear how optimal model complexity should be assessed, and whether this optimal complexity has been affected by recent advances in modelling methodology. This issue is particularly relevant to small populations because they are subject to complex dynamics but inferences about those dynamics are often constrained by small sample sizes. We fitted Bayesian hierarchical models to long-term data on vital rates (survival and reproduction) for the toutouwai Petroica longipes population reintroduced to Tiritiri Matangi, a 220-ha New Zealand island, and quantified the performance of those models in terms of their likelihood of replicating the observed population dynamics. These dynamics consisted of overall growth from 33 (±0.3) to 160 (±6) birds from 1992-2018, including recoveries following five harvest events for further reintroductions to other sites. We initially included all factors found to affect vital rates, which included inbreeding, post-release effects (PRE), density-dependence, sex, age and random annual variation, then progressively removed these factors. We also compared performance of models where data analysis and simulations were done simultaneously to those produced with the traditional two-step approach, where vital rates are estimated first then fed into a separate simulation model. Parametric uncertainty and demographic stochasticity were incorporated in all projections. The essential factors for replicating the population's dynamics were density-dependence in juvenile survival and PRE, i.e. initial depression of survival and reproduction in translocated birds. Inclusion of other factors reduced the precision of projections, and therefore the likelihood of matching observed dynamics. However, this reduction was modest when the modelling was done in an integrated framework. In contrast, projections were much less precise when done with a two-step modelling approach, and the cost of additional parameters was much higher under the two-step approach. These results suggest that minimization of complexity may be less important than accounting for covariances in parameter estimates, which is facilitated by integrating data analysis and population projections using Bayesian methods.

Integrated mechanistic and data-driven modeling for risk assessment of greenhouse gas production in an urbanized river system

Surrounded by intense anthropogenic activities, urban polluted rivers have increasingly been reported as a significant source of greenhouse gases (GHGs). However, unlike pollution and climate change, no integrated urban water models have investigated the GHG production in urban rivers due to system complexity. In this study, we proposed a novel integrated framework of mechanistic and data-driven models to qualitatively assess the risks of GHG accumulation in an urban river system in different water management interventions. Particularly, the mechanistic model delivered elaborated insights into river states in four intervention scenarios in which the installation of a new wastewater treatment plant using two different technologies, together with new sewage systems and additional retention tanks, were assessed during dry and rainy seasons. From the insights, we applied fuzzy rule-based models as a decision support tool to predict the GHG accumulation risks and identify their driving factors in the scenarios. The obtained results indicated the important role of new discharge connection and additional storage capacity in decreasing pollutant concentrations, consequently, reducing the risks. Moreover, among the major variables explaining the GHG accumulation in the rivers, DO level was considerably affected by the reaeration capacity of the rivers that was strongly dependent on river slope and flow. Furthermore, river water quality emerged as the most critical variable explaining the pCO₂ and N₂O accumulation that implied that the more polluted and anaerobic the sites were, the higher were their GHG accumulation. Given its simplicity and transparency, the proposed modeling framework can be applied to other river basins as a decision support tool in setting up integrated urban water management plans.

A Simplified Population-Level Landscape Model Identifying Ecological Risk Drivers of Pesticide Applications, Part One: Case Study for Large Herbivorous Mammals

Environmental risk assessment is a key process for the authorization of pesticides, and is subjected to continuous challenges and updates. Current approaches are based on standard scenarios and independent substance-crop assessments. This arrangement does not address the complexity of agricultural ecosystems with mammals feeding on different crops. This work presents a simplified model for regulatory use addressing landscape variability, co-exposure to several pesticides, and predicting the effect on population abundance. The focus is on terrestrial vertebrates and the aim is the identification of the key risk drivers impacting on mid-term population dynamics. The model is parameterized for EU assessments according to the European Food Safety Authority (EFSA) Guidance Document, but can be adapted to other regulatory schemes. The conceptual approach includes two modules: (a) the species population dynamics, and (b) the population impact of pesticide exposure. Population dynamics is modelled through daily survival and seasonal reproductions rates; which are modified in case of pesticide exposure. All variables, parameters, and functions can be modified. The model has been calibrated with ecological data for wild rabbits and brown hares and tested for two herbicides, glyphosate and bromoxynil, using validated toxicity data extracted from EFSA assessments. Results demonstrate that the information available for a regulatory assessment, according to current EU information requirements, is sufficient for predicting the impact and possible consequences at population dynamic levels. The model confirms that agroecological parameters play a key role when assessing the effect of pesticide exposure on population abundance. The integration of laboratory toxicity studies with this simplified landscape model allows for the identification of conditions leading to population vulnerability or resilience. An Annex includes a detailed assessment of the model characteristics according to the EFSA scheme on Good Modelling Practice.

Pop-guide: Population modeling guidance, use, interpretation, and development for ecological risk assessment

The assimilation of population models into ecological risk assessment (ERA) has been hindered by their range of complexity, uncertainty, resource investment, and data availability. Likewise, ensuring that the models address risk assessment objectives has been challenging. Recent research efforts have begun to tackle these challenges by creating an integrated modeling framework and decision guide to aid the development of population models with respect to ERA objectives and data availability. In the framework, the trade-offs associated with the generality, realism, and precision of an assessment are used to guide the development of a population model commensurate with the protection goal. The decision guide provides risk assessors with a stepwise process to assist them in developing a conceptual model that is appropriate for the assessment objective and available data. We have merged the decision guide and modeling framework into a comprehensive approach, Population modeling Guidance, Use, Interpretation, and Development for Ecological risk assessment (Pop-GUIDE), for the development of population models for ERA that is applicable across regulatory statutes and assessment objectives. In Phase 1 of Pop-GUIDE, assessors are guided through the trade-offs of ERA generality, realism, and precision, which are translated into model objectives. In Phase 2, available data are assimilated and characterized as general, realistic, and/or precise. Phase 3 provides a series of dichotomous questions to guide development of a conceptual model that matches the complexity and uncertainty appropriate for the assessment that is in concordance with the available data. This phase guides model developers and users to ensure consistency and transparency of the modeling process. We introduce Pop-GUIDE as the most comprehensive guidance for population model development provided to date and demonstrate its use through case studies using fish as an example taxon and the US Federal Insecticide Fungicide and Rodenticide Act and Endangered Species Act as example regulatory statutes. Integr Environ Assess Manag 2021;17:767-784. © 2020 SETAC. This article has been contributed to by US Government employees and their work is in the public domain in the USA.

Back to the future: Reintegrating biology to understand how past eco-evolutionary change can predict future outcomes

During the last few decades, biologists have made remarkable progress in understanding the fundamental processes that shape life. But despite the unprecedented level of knowledge now available, large gaps still remain in our understanding of the complex interplay of eco-evolutionary mechanisms across scales of life. Rapidly changing environments on Earth provide a pressing need to understand the potential implications of eco-evolutionary dynamics, which can be achieved by improving existing eco-evolutionary models and fostering convergence among the sub-fields of biology. We propose a new, data-driven approach that harnesses our knowledge of the functioning of biological systems to expand current conceptual frameworks and develop corresponding models that can more accurately represent and predict future eco-evolutionary outcomes. We suggest a roadmap toward achieving this goal. This long-term vision will move biology in a direction that can wield these predictive models for scientific applications that benefit humanity and increase the resilience of natural biological systems. We identify short, medium, and long-term key objectives to connect our current state of knowledge to this long-term vision, iteratively progressing across three stages: 1) utilizing knowledge of biological systems to better inform eco-evolutionary models, 2) generating models with more accurate predictions, and 3) applying predictive models to benefit the biosphere. Within each stage, we outline avenues of investigation and scientific applications related to the timescales over which evolution occurs, the parameter space of eco-evolutionary processes, and the dynamic interactions between these mechanisms. The ability to accurately model, monitor, and anticipate eco-evolutionary changes would be transformational to humanity's interaction with the global environment, providing novel tools to benefit human health, protect the natural world, and manage our planet's biosphere.

Is New Always Better? Frontiers in Global Climate Datasets for Modeling Treeline Species in the Himalayas

Comparing and evaluating global climate datasets and their effect on model performance in regions with limited data availability has received little attention in ecological modeling studies so far. In this study, we aim at comparing the interpolated climate dataset Worldclim 1.4, which is the most widely used in ecological modeling studies, and the quasi-mechanistical downscaled climate dataset Chelsa, as well as their latest versions Worldclim 2.1 and Chelsa 1.2, with regard to their suitability for modeling studies. To evaluate the effect of these global climate datasets at the meso-scale, the ecological niche of Betula utilis in Nepal is modeled under current and future climate conditions. We underline differences regarding methodology and bias correction between Chelsa and Worldclim versions and highlight potential drawbacks for ecological models in remote high mountain regions. Regarding model performance and prediction plausibility under current climatic conditions, Chelsa-based models significantly outperformed Worldclim-based models, however, the latest version of Chelsa contains partially inherent distorted precipitation amounts. This study emphasizes that unmindful usage of climate data may have severe consequences for modeling treeline species in high-altitude regions as well as for future projections, if based on flawed current model predictions. The results illustrate the inevitable need for interdisciplinary investigations and collaboration between climate scientists and ecologists to enhance climate-based ecological model quality at meso- to local-scales by accounting for local-scale physical features at high temporal and spatial resolution.

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.

Uncertainty in and around biophysical modelling: insights from interdisciplinary research on agricultural digitalization

Agricultural digitalization is providing growing amounts of real-time digital data. Biophysical simulation models can help interpret these data. However, these models are subject to complex uncertainties, which has prompted calls for interdisciplinary research to better understand and communicate modelling uncertainties and their impact on decision-making. This article develops two corresponding insights from an interdisciplinary project in a New Zealand agricultural research organization. First, we expand on a recent Royal Society Open Science journal article (van der Bles et al. 2019 Royal Society Open Science 6, 181870 (doi:10.1098/rsos.181870)) and suggest a threefold conceptual framework to describe direct, indirect and contextual uncertainties associated with biophysical models. Second, we reflect on the process of developing this framework to highlight challenges to successful collaboration and the importance of a deeper engagement with interdisciplinarity. This includes resolving often unequal disciplinary standings and the need for early collaborative problem framing. We propose that both insights are complementary and informative to researchers and practitioners in the field of modelling uncertainty as well as to those interested in interdisciplinary environmental research generally. The article concludes by outlining limitations of interdisciplinary research and a shift towards transdisciplinarity that also includes non-scientists. Such a shift is crucial to holistically address uncertainties associated with biophysical modelling and to realize the full potential of agricultural digitalization.

Using convolutional neural network for predicting cyanobacteria concentrations in river water

Machine learning modeling techniques have emerged as a potential means for predicting algal blooms. In this study, synthetic spatio-temporal water quality data for a river section were generated with a 3D water quality model and used to investigate the capability of a convolutional neural network (CNN) for predicting harmful cyanobacterial blooms. The CNN model displayed a reasonable capacity for short-term predictions of cyanobacteria (Microcystis) biomass. In the nowcasting of Microcystis, the CNN performance had a Nash-Sutcliffe Efficiency (NSE) of 0.87. An increase in the forecast lead time resulted in a decrease in the prediction accuracy, reducing the NSE from 0.87 to 0.58. As the spatial observation density increased from 20% to 100% of the input image grids, the CNN prediction NSE had improved from 0.70 to 0.84. Adding noise to the data resulted in accuracy deterioration, but even at the noise amplitude of 10%, the accuracy was acceptable for some applications, with NSE = 0.76. Visualization of the CNN results characterized its performance variations across the studied river reach. Overall, this study successfully demonstrated the capability of the CNN model for cyanobacterial bloom prediction using high temporal frequency images.

Ecological and human health risk assessment

The literature review presented in this paper includes the ecological and human health risk assessment in the form of receptors in the environment. The main objective of this review to highlight a summary of the many studies undertaken in the year 2019. The first part of the review covers the papers published on the health risk assessment related to human and ecological health. This article focuses on methods and tools utilized for the analysis of scientific studies and the data. The review provides main issues such as interpretation of data, uncertainty, and policies related to the management of risks. The ecological and human health risk assessment is divided into two main sections. Each of these sections presents in broad the risk assessment process namely pollution studies, remediation, and tools required for the management of natural resources and the environment.