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[This corrects the article DOI: 10.1038/s41558-023-01746-w.].

Predicting how ecological communities will respond to disturbances is notoriously challenging, especially given the variability in species' responses within the same community. Focusing solely on aggregate responses may obscure extinction risks for certain species owing to compensatory effects, emphasizing the need to understand the drivers of the response variability at the species level. Yet, these drivers remain poorly understood. Here, we reveal that despite the typical complexity of biotic interaction networks, species' responses follow a discernible pattern. Specifically, we demonstrate that the species whose population abundances are most reduced by biotic interactions—which are not always the rarest species—are those that exhibit the strongest responses to disturbances. This insight enables us to pinpoint sensitive species within communities without requiring precise information about biotic interactions. Our novel approach introduces avenues for future research aimed at identifying sensitive species and elucidating their impacts on entire communities. [ABSTRACT FROM AUTHOR]

ABSTRACT The use of monitoring data to build prediction models for the abundance and activity of necrophagous blow flies is common practice in forensic entomology, but its advantages and disadvantages are still debated. A frequently asked question is the transferability of such species prediction models. So far no study has examined whether the assumption of low transferability of these data between cities and seasons holds true. In the present study, we evaluate whether models calibrated with a specific training data set from a specific place and time can be transferred to other data sets for different time periods and locations. We developed models using five different algorithms to predict the activity and abundance of four forensically relevant blow fly species (Calliphora vicina Robineau‐Desvoidy, Lucilia ampullacea Villeneuve, Lucilia caesar (Linnaeus), Lucilia sericata (Meigen)). The training data set was obtained from a single city, and the transferability of the models was evaluated using monitoring data from this and three other cities. The geographic transferability of the models was confirmed for all algorithms, but only for two species, C. vicina and L. sericata, and for two of the four cities. Lucilia caesar and L. ampullacea were rare in the test data set, and their species‐specific adaptation to environmental parameters was not captured by the models. Cities that did not work differed from the training data set in terms of climate and habitat features. To build generalised predictive models of blow fly abundance and activity, we need training data sets based on monitoring data from different regions, seasons and years to cover a wide range of environmental conditions. This is essential for describing and predicting natural variability. [ABSTRACT FROM AUTHOR]

Aim: Marginal tree populations, either those located at the edges of the species' range or in suboptimal environments, are often a valuable genetic resource for biological conservation. However, there is a lack of knowledge about the genetic consequences of population marginality, estimated across entire species' ranges. Our study addresses this gap by providing information about several genetic indicators and their variability in marginal and core populations identified using quantitative marginality indices. Location: Southwestern Europe and North Africa. Methods: Using 10,185 SNPs across 82 populations of maritime pine (Pinus pinaster Ait.), a widespread conifer characterised by a fragmented range, we modelled the relationship of seven genetic indicators potentially related to population evolutionary resilience, namely genetic diversity (based on both all SNPs and outlier SNPs), inbreeding, genetic differentiation, recessive genetic load and genomic offset, with population geographical, demo‐historical and ecological marginality (as estimated by nine quantitative indices). Models were constructed for both regional (introducing gene pool as a random factor) and range‐wide spatial scales. Results: We showed a trend towards decreasing overall genetic diversity and increasing differentiation with geographic marginality, supporting the centre‐periphery hypothesis (CPH). However, we found no correlation between population inbreeding and marginality, while geographically marginal populations had a lower recessive genetic load (only models without the gene pool effect). Ecologically marginal populations had a higher genomic offset, suggesting higher maladaptation to future climate, albeit some of these populations also had high genetic diversity for climate outliers. Main Conclusions: Overall genetic diversity (but not outlier‐based estimates) and differentiation patterns support the CPH. Ecologically marginal populations and those at the southern edge could be more vulnerable to climate change due to higher climate maladaptation, as predicted by genomic offsets, and/or lower potentially adaptive genetic diversity. This risk is exacerbated by typically small effective population sizes and increasing human impact in marginal populations. [ABSTRACT FROM AUTHOR]

ABSTRACT Big biodiversity data sets have great potential for monitoring and research because of their large taxonomic, geographic and temporal scope. Such data sets have become especially important for assessing temporal changes in species' populations and distributions. Gaps in the available data, especially spatial and temporal gaps, often mean that the data are not representative of the target population. This hinders drawing large‐scale inferences, such as about species' trends, and may lead to misplaced conservation action. Here, we conceptualise gaps in biodiversity monitoring data as a missing data problem, which provides a unifying framework for the challenges and potential solutions across different types of biodiversity data sets. We characterise the typical types of data gaps as different classes of missing data and then use missing data theory to explore the implications for questions about species' trends and factors affecting occurrences/abundances. By using this framework, we show that bias due to data gaps can arise when the factors affecting sampling and/or data availability overlap with those affecting species. But a data set per se is not biased. The outcome depends on the ecological question and statistical approach, which determine choices around which sources of variation are taken into account. We argue that typical approaches to long‐term species trend modelling using monitoring data are especially susceptible to data gaps since such models do not tend to account for the factors driving missingness. To identify general solutions to this problem, we review empirical studies and use simulation studies to compare some of the most frequently employed approaches to deal with data gaps, including subsampling, weighting and imputation. All these methods have the potential to reduce bias but may come at the cost of increased uncertainty of parameter estimates. Weighting techniques are arguably the least used so far in ecology and have the potential to reduce both the bias and variance of parameter estimates. Regardless of the method, the ability to reduce bias critically depends on knowledge of, and the availability of data on, the factors creating data gaps. We use this review to outline the necessary considerations when dealing with data gaps at different stages of the data collection and analysis workflow. [ABSTRACT FROM AUTHOR]

There is growing concern about the response of contemporary ecosystems to increasing and novel anthropogenic pressures and environmental conditions. Palaeoecology is crucial to understanding how ecosystems have responded to past environmental changes and can inform management of contemporary ecosystems and contribute to forecasts of ecosystem responses to change. However, palaeoecological data are subject to uncertainties that arise from environmental processes, field and laboratory methods, and data processing, and that affects inferences drawn from them. Understanding how different sources of uncertainty affect the analyses of proxy records remains limited, and records are often interpreted solely qualitatively. We present a virtual ecology approach for assessing how uncertainties inherent in empirical proxy data influence statistical analyses and the inferences drawn from them. In the virtual ecology approach, both the data and the observational process are recreated in simulation to assess sampling and analytical methods. We demonstrate results from a new model for simulating core-type samples of pseudoproxies comparable to empirical proxy data but not subject to the same sources of proxy and chronological uncertainties. These 'error-free' pseudoproxies generated under known driving conditions have uncertainties (e.g. core mixing, sub-sampling, and proxy quantification) systematically introduced to them to assess how individual and combined sources of uncertainty influence analytical methods. Results indicate that inferences drawn from statistical analysis, such as the stability of a system, or the rate of ecological turnover, can change substantially between the 'error-free' pseudoproxies, and degraded and sub-sampled data. We show how our approach can advance understanding of uncertainties in palaeoecological data and how it can help shape research questions by quantifying of their influence on proxy data. [ABSTRACT FROM AUTHOR]

There is an increasing need for ecosystem‐level distribution models (EDMs) and a better understanding of which factors affect their quality. We investigated how the performance and transferability of EDMs are influenced by 1) the choice of predictors and 2) model complexity. We modelled the distribution of 15 pre‐classified ecosystem types in Norway using 252 predictors gridded to 100 × 100 m resolution. The ecosystem types are major types in the 'Nature in Norway' system mainly defined by rule‐based criteria such as whether soil or specific functional groups (e.g. trees) are present. The predictors were categorised into four groups, of which three represented proxies for natural, anthropogenic, or terrain processes ('ecological predictors') and one represented spectral and structural characteristics of the surface observable from above ('surface predictors'). Models were generated for five levels of model complexity. Model performance and transferability were evaluated with data collected independently of the training data. We found that 1) models trained with surface predictors only performed considerably better and were more transferable than models trained with ecological predictors, and 2) model performance increased with model complexity, levelling off from approximately 10 parameters and reaching a peak at approximately 20 parameters, while model transferability decreased with model complexity. Our findings suggest that surface predictors enhance EDM performance and transferability, most likely because they represent discernible surface characteristics of the ecosystem types. A poor match between the rule‐based criteria that define the ecosystem types and the ecological predictors, which represent ecological processes, is a plausible explanation for why surface predictors better predict the distribution of ecosystem types. Our results indicate that, in most cases, the same models are not well suited for contrasting purposes, such as predicting where ecosystems are and explaining why they are there. [ABSTRACT FROM AUTHOR]

European grassland management has often favoured high production through frequent mowing and heavy fertilisation over biodiversity conservation, which is typically supported by less intensive management. Besides management, climate change and extremes are increasingly affecting grassland productivity and biodiversity, requiring timely adaptation of management practices. Here, we describe the development of a prototype Digital Twin (pDT) of grassland biodiversity dynamics intended to support researchers, farmers or regulatory decision-makers in monitoring the current state of selected grassland sites and projecting their future state under various management and climate scenarios.

Abstract Key message A validation convention can be established for forest management optimization models. It consists of (1) the delivery of face validation, (2) performing at least one other validation technique, and (3) an explicit discussion of how the optimization model fulfills the stated purpose. Validation by potential users or external experts is of high importance. Context Optimization modeling has long assisted the management of forest ecosystems, but the credibility of these models has always been debated with criticisms concerning data quality, failures to include relevant processes in the scope of models, and the inclusion of unrealistic assumptions. Validation is widely considered to be crucial to establishing the credibility of models in general, but how to validate optimization models in particular represents a permanent question generally in operations research. Aims We aim to synthesize practical recommendations for the development of validation frameworks in the optimization modeling for forest management. Methods We selected a sample of 46 studies devoted to optimization models to be applied in practice, analysed the contents with respect to validation, and provided a critical review. Results We (1) clarified the meaning and usage of different validation-related terms that are commonly encountered in the literature, (2) identified and categorised the various methods and frameworks that are used to demonstrate model credibility, and (3) derived organizing principles that helped to suggest improvements in validation frameworks. Conclusions A practical validation convention can be established and we suggest the convention to consist of three stages. By providing structured and consistent information about validation processes, researchers in forest management optimization can better demonstrate the credibility of their work to readers and potential users.

Restoring biodiversity‐based resilience and ecosystem multi‐functionality needs to be informed by more accurate predictions of animal biodiversity responses to environmental change. Ecological models make a substantial contribution to this understanding, especially when they encode the biological mechanisms and processes that give rise to emergent patterns (population, community, ecosystem properties and dynamics). Here, a distinction between 'mechanistic' and 'process‐based' ecological models is established to review existing approaches. Mechanistic and process‐based ecological models have made key advances to understanding the structure, function and dynamics of animal biodiversity, but are typically designed to account for specific levels of biological organisation and spatiotemporal scales. Cross‐scale ecological models, which predict emergent co‐occurring biodiversity patterns at interacting scales of space, time and biological organisation, is a critical next step in predictive ecology. A way forward is to first capitalise on existing models to systematically evaluate the ability of scale‐explicit mechanisms and processes to predict emergent patterns at alternative scales. Such model intercomparisons will reveal mechanism to process transitions across fine to broad scales, overcome approach‐specific barriers to model realism or tractability and identify gaps which necessitate the development of new fundamental principles. Key challenges surrounding model complexity and uncertainty would need to be addressed, and while opportunities from big data can streamline the integration of multiple scale‐explicit biodiversity patterns, ambitious cross‐scale field studies are also needed. Crucially, overcoming cross‐scale ecological modelling challenges would unite disparate fields of ecology with the common goal of improving the evidence‐base to safeguard biodiversity and ecosystems under novel environmental change. [ABSTRACT FROM AUTHOR]

European grassland management has often favoured high production through frequent mowing and heavy fertilisation over biodiversity conservation, which is typically supported by less intensive management. Besides management, climate change and extremes are increasingly affecting grassland productivity and biodiversity, requiring timely adaptation of management practices. Here, we describe the development of a prototype Digital Twin (pDT) of grassland biodiversity dynamics intended to support researchers, farmers or regulatory decision-makers in monitoring the current state of selected grassland sites and projecting their future state under various management and climate scenarios. [ABSTRACT FROM AUTHOR]

African swine fever is a transmissible virus impacting wild and domestic swine populations. In Europe, it is non-native and the recently introduced genotype affects wild boar populations with occasional outbreaks in domestic pigs. The ability to predict short-term spatial dynamics of this disease will greatly improve our ability to control and limit future spread of the virus. The BioDT disease outbreaks prototype, currently in development, implements an individual-based landscape-level mechanistic model as a digital twin aimed at providing informed support for management decisions in response to the spread of African swine fever in European wild boar populations. [ABSTRACT FROM AUTHOR]

Key message: A validation convention can be established for forest management optimization models. It consists of (1) the delivery of face validation, (2) performing at least one other validation technique, and (3) an explicit discussion of how the optimization model fulfills the stated purpose. Validation by potential users or external experts is of high importance. Context: Optimization modeling has long assisted the management of forest ecosystems, but the credibility of these models has always been debated with criticisms concerning data quality, failures to include relevant processes in the scope of models, and the inclusion of unrealistic assumptions. Validation is widely considered to be crucial to establishing the credibility of models in general, but how to validate optimization models in particular represents a permanent question generally in operations research. Aims: We aim to synthesize practical recommendations for the development of validation frameworks in the optimization modeling for forest management. Methods: We selected a sample of 46 studies devoted to optimization models to be applied in practice, analysed the contents with respect to validation, and provided a critical review. Results: We (1) clarified the meaning and usage of different validation-related terms that are commonly encountered in the literature, (2) identified and categorised the various methods and frameworks that are used to demonstrate model credibility, and (3) derived organizing principles that helped to suggest improvements in validation frameworks. Conclusions: A practical validation convention can be established and we suggest the convention to consist of three stages. By providing structured and consistent information about validation processes, researchers in forest management optimization can better demonstrate the credibility of their work to readers and potential users. [ABSTRACT FROM AUTHOR]

Climate change has driven agriculture to alter farming methods for food production. This paper presents a new concept for monitoring, acquisition, management, analysis, and synthesis of ecological data, which captures the environmental determinants and direct gradients suited to a particular requirement for specific plant cultivation and sustainable agriculture. The purpose of this study is to investigate a smart seablite cultivation system. A novel digital agricultural method was developed and applied to digitised seablite cultivation. Machine learning was used to predict the future growth conditions of plants (seablites). The study identified the illustrative maps of seablite origins, a conceptual seablite smart farming model, essential factors for growing seablite, a digital circuit for cultivating seablite, and digital data of seablite growth phases comprised the digital data. The findings indicate that: (1) An indicator of soil salinity is a quantity of sodium chloride extracted from a seablite sample indicating its origin of environmental determinants. (2) Saline soil, saline water, pH, moisture, temperature, and sunlight are essential factors for seablite development. These factors are dependent on climate change and were measured using a smart seablite cultivation system. (3) Digital circuits of seablite cultivation provide a better understanding of the relationship between the essential factors for seablite growth and seablite growth phases. (4) Deep neural networks outperformed vector machines, with 86% accuracy at predicting future growth of seablites. Therefore, this finding showed that the essential seablite development factors can be manipulated as key controllers for agriculture in response to climate change and agriculture can be planned. Basic digitisation of specific plants aids plant migration. Digital agriculture is an important practice for agroecosystems.

There are obstacles in estimating environmental dynamics behind its convenience, beginning with the development of effective policies for sustainable urban development. The objectives of this research were to comprehend the ability and performance of ecological indices integration and to identify the spatial distribution of changes from 2018 to 2021 in Pekanbaru City, Riau province, Indonesia. This study employed remote sensing data to create ecological parameters including the build-up index, vegetation index, soil index, and moisture index, as well as principal component analysis to generate ecological index integration. The findings indicate a correlation of over 90% among these parameters from 2018 to 2021. Overall, there has been a significant decrease in the ecological quality index’s high-quality categories, such as good and excellent, covering a total of 19.6% over 127 km². Conversely, the poor ecological quality category increased to 2.2%, encompassing an area of 15 km², up from the initial 21.2% covering 122 km². Additionally, the fair and moderate categories also experienced increases of 4% and 13.4%, respectively, reaching 28 km² and 84 km². The study area’s ecological quality is largely affected by increased anthropogenic activities, leading to a drastic decrease in the presence of ecological quality in the good and excellent categories. The importance of spatial planning is emphasized to incorporate aspects of ecological assessment rather than solely focusing on increasing economic activity. This outcome can be used to respond to the concept of sustainable development by caring for the ecological environment, particularly in urban areas, and mitigating ecological damage.

Rising temperatures are impacting the range and prevalence of mosquito-borne diseases. A promising biocontrol technology replaces wild mosquitoes with those carrying the virus-blocking Wolbachia bacterium. Because the most widely used strain, wMel, is adversely affected by heat stress, we examined how global warming may influence wMel-based replacement. We simulated interventions in two locations with successful field trials using Coupled Model Intercomparison Project Phase 5 climate projections and historical temperature records, integrating empirical data on wMels thermal sensitivity into a model of Aedes aegypti population dynamics to evaluate introgression and persistence over one year. We show that in Cairns, Australia, climatic futures necessitate operational adaptations for heatwaves exceeding two weeks. In Nha Trang, Vietnam, projected heatwaves of three weeks and longer eliminate wMel under the most stringent assumptions of that symbionts thermal limits. We conclude that this technology is generally robust to near-term (2030s) climate change. Accelerated warming may challenge this in the 2050s and beyond.

Mammalian herbivores play a pivotal role in Earth System processes by affecting biogeochemical cycles and ecosystem functioning, potentially leading to significant repercussions on atmosphere–biosphere feedbacks. Global dynamic models of mammalian populations can improve our understanding of their ecological role at large scales and the consequences of their extinctions. However, such models are still lacking and mammals are poorly integrated in Earth System Science.We developed a mechanistic global model of terrestrial herbivore populations simulated with 37 functional groups defined through the analysis of eco‐physiological traits across all extant herbivores (2599 species). We coupled this model with a global vegetation model to predict herbivores' maximum potential biomass in pre‐industrial and at present‐day and to study the environmental drivers explaining the distribution of herbivore biomass. Present‐day biomass was estimated by accounting for anthropogenic activity causing habitat and range losses.We show that natural ecosystems could have sustained a potential wild herbivore wet biomass of 330 Mt (95% CI: 245–417), comprised of 193 Mt (95% CI: 177–208) by large species (body mass >1–10 kg, depending on functional group) and 138 Mt (95% CI: 68–209) by small species. We estimate that the remaining present‐day large herbivores biomass is 82 Mt (95% CI: 32–133), reduced by 57% due to anthropogenic activity; consequently, small herbivores currently dominate global herbivore biomass with 98 Mt (95% CI: 91–106, −29%). Losses vary greatly across climatic zones and functional groups, suggesting that size is not the only discriminant feature of biomass decline.Actual evapotranspiration is the most important driver of total, large and small herbivore biomass and explains 64%, 59% and 49% of its variation, respectively. Distribution of modelled and observed large herbivores' biomass suggested a high dependency on energy and water with more biomass in hot and wet areas. These results challenge the notion that large herbivore biomass peaks primarily in ecosystems with intermediate precipitation levels such as savannas.Outside Africa and the Tropics, pre‐industrial biomass hotspots occur in areas today dominated by humans; this could undermine the recovery of larger species biomass in certain areas. Our herbivore biomass estimates provide a quantitative benchmark for setting conservation and rewilding goals at large spatial scales. The herbivore model and functional classification create new opportunities to integrate mammals into Earth System Science and models. Read the free Plain Language Summary for this article on the Journal blog. [ABSTRACT FROM AUTHOR]

Aquatic species show different sensitivities and responses to chronic thermal stress, resulting in varying degrees of resistance to the negative impacts of climate change, which are ultimately expressed as range expansions or contractions. The choice of species appropriate for assessing climate change impacts in aquatic ecosystems should be guided by the robustness of the relationship between a chosen chronic stress thermal threshold and associated habitat contraction. Twelve aquatic species were evaluated as potential climate change indicators, from which six were selected for testing a conceptual framework for predicting the degree of utility of a species as a climate change indicator. Results indicate that species with a chronic biological thermal threshold below 20°C are likely to experience in excess of 50% loss of thermally suitable environment. Cooler thermal thresholds could inform the choice of suitable sentinel species for use as early indicators of chronic thermal stress, while thresholds above this reflect increasingly thermally resistant species within aquatic communities. [ABSTRACT FROM AUTHOR]

Ecological Niche Models (ENMs) are often used to project species distributions within alien ranges and in future climatic scenarios. However, ENMs depend on species‐environment equilibrium, which may be absent for actively expanding species. We present a novel framework to estimate whether species have reached environmental equilibrium in their native and alien ranges. The method is based on the estimation of niche breadth with the accumulation of species occurrences. An asymptote will indicate exhaustive knowledge of the realised niches. We demonstrate the CNA framework for 26 species of mammals, amphibians, and birds. Possible outcomes of the framework include: (1) There is enough data to quantify the native and alien realised niches, allowing us to calculate niche expansion between the native and alien ranges, also indicating that ENMs can be reliably projected to new environmental conditions. (2) The data in the native range is not adequate but an asymptote is reached in the alien realised niche, indicating low confidence in our ability to evaluate niche expansion in the alien range but high confidence in model projections to new environmental conditions within the alien range. (3) There is enough data to quantify the native realised niche, but not enough knowledge about the alien realised niche, hindering the reliability of projections beyond sampled conditions. (4) Both the native and alien ranges do not reach an asymptote, and thus few robust conclusions about the species' niche or future projections can be made. Our framework can be used to detect species' environmental equilibrium in both the native and alien ranges, to quantify changes in the realised niche during the invasion processes, and to estimate the likely accuracy of model projections to new environmental conditions. [ABSTRACT FROM AUTHOR]

Abstract In building a robust knowledge base or validating existing models for use in ecological spatial modelling, having plentiful high‐quality data is paramount. Careful survey design helps attain that goal and, in part due to financial constraints, such design requires the balancing of hard monetary costs and the intangible benefit of improved ecological models. We propose a framework that quantifies a location's value to the modeller by accounting for both the probability of obtaining new samples and their expected contribution to the model. The approach is illustrated on a citizen science database of roadkills in Taiwan, modelled as a Poisson point process on a linear road network. Our method has revealed some valuable locations that were not self‐evident, for example, highlighting the possibility of sending volunteers to mountainous areas that despite being hard to reach, would provide valuable samples. We have also highlighted some ex situ sampling opportunities to avoid wasting resources by over‐sampling hard to access locations. Our technique is not restricted to presence‐only data, and in fact we present a general framework that can be applied to a wide range of settings by tuning its formulation. Our method is quite flexible and allows for more elaborate value functions, enabling managers to precisely quantify varied goals within the same framework.

In building a robust knowledge base or validating existing models for use in ecological spatial modelling, having plentiful high‐quality data is paramount. Careful survey design helps attain that goal and, in part due to financial constraints, such design requires the balancing of hard monetary costs and the intangible benefit of improved ecological models.We propose a framework that quantifies a location's value to the modeller by accounting for both the probability of obtaining new samples and their expected contribution to the model. The approach is illustrated on a citizen science database of roadkills in Taiwan, modelled as a Poisson point process on a linear road network.Our method has revealed some valuable locations that were not self‐evident, for example, highlighting the possibility of sending volunteers to mountainous areas that despite being hard to reach, would provide valuable samples. We have also highlighted some ex situ sampling opportunities to avoid wasting resources by over‐sampling hard to access locations.Our technique is not restricted to presence‐only data, and in fact we present a general framework that can be applied to a wide range of settings by tuning its formulation. Our method is quite flexible and allows for more elaborate value functions, enabling managers to precisely quantify varied goals within the same framework. [ABSTRACT FROM AUTHOR]

We studied the community structure of crustacean zooplankton along the biogeographical zones of the St. Lawrence River (Québec, Canada), to evaluate how the riverscale hydrological network formed by water masses, and local-scale aquatic environment, influenced the distribution of crustacean groups (cladocerans, calanoids, cyclopoids and harpacticoids) during the spring (high discharge) and summer (low discharge) hydroperiods. Zooplankton and environmental data were sampled at 52 sites forming 16 transects along the fluvial section zone (FSZ), the fluvial estuary zone (FEZ) and the estuarine transition zone (ETZ) of the St. Lawrence River in May and August 2006. We compared zooplankton community composition among biogeographical zones and fluvial lakes and across the fluvial estuarine continuum. Analyses were carried out using asymmetric eigenvector maps (AEM), redundancy analysis (RDA), and variation partitioning. Spatial distribution patterns revealed a complex river model. Riverscale discontinuities between the fluvial and estuarine transition zones, and the hydrological network of water masses in the fluvial zones, explained better the spatial zooplankton distribution patterns along the fluvial estuarine continuum than the local environment. Spatial variation in the flow network and environmental conditions were the main drivers of zooplankton distributions in spring whereas the flow network of water masses was the most influential factor in summer. [ABSTRACT FROM AUTHOR]

Steady states of dynamical systems, whether stable or unstable, are critical for understanding future evolution. Robust steady states, ones that persist under small changes in the model parameters, are desired when modelling ecological systems, where it is common for accurate and detailed information on functional form and parameters to be unavailable. Previous work by Jahedi et al. [Robustness of solutions of almost every system of equations, SIAM J. Appl. Math. 82(5) (2022), pp. 1791–1807; Structured systems of nonlinear equations, SIAM J. Appl. Math. 83(4) (2023), pp. 1696–1716.] has established criteria to imply the prevalence of robust steady states for systems with minimal predetermined structure, including conventional structured systems. We review that work and extend it by allowing symmetries in the system structure, which present added obstructions to robustness. [ABSTRACT FROM AUTHOR]

Abstract Ecological Niche Models (ENMs) are often used to project species distributions within alien ranges and in future climatic scenarios. However, ENMs depend on species‐environment equilibrium, which may be absent for actively expanding species. We present a novel framework to estimate whether species have reached environmental equilibrium in their native and alien ranges. The method is based on the estimation of niche breadth with the accumulation of species occurrences. An asymptote will indicate exhaustive knowledge of the realised niches. We demonstrate the CNA framework for 26 species of mammals, amphibians, and birds. Possible outcomes of the framework include: (1) There is enough data to quantify the native and alien realised niches, allowing us to calculate niche expansion between the native and alien ranges, also indicating that ENMs can be reliably projected to new environmental conditions. (2) The data in the native range is not adequate but an asymptote is reached in the alien realised niche, indicating low confidence in our ability to evaluate niche expansion in the alien range but high confidence in model projections to new environmental conditions within the alien range. (3) There is enough data to quantify the native realised niche, but not enough knowledge about the alien realised niche, hindering the reliability of projections beyond sampled conditions. (4) Both the native and alien ranges do not reach an asymptote, and thus few robust conclusions about the species’ niche or future projections can be made. Our framework can be used to detect species’ environmental equilibrium in both the native and alien ranges, to quantify changes in the realised niche during the invasion processes, and to estimate the likely accuracy of model projections to new environmental conditions.

This article is an editorial discussing the topic of ecological modeling in population, community, and ecosystem dynamics. The authors explain that the purpose of the special topic was to gather a sample of modeling works to understand the current trends and research directions in the field. The articles covered a wide range of topics, including evolutionary dynamics, biogeochemical cycles, species interactions, and uncertainty. The authors highlight the underlying concern of uncertainty in the majority of the articles and emphasize the importance of understanding and including uncertainty in ecological theories. They conclude by encouraging readers to find inspiration for their own research from the published articles and to contribute to tackling uncertainty in ecological systems. [Extracted from the article]

Steady states of dynamical systems, whether stable or unstable, are critical for understanding future evolution. Robust steady states, ones that persist under small changes in the model parameters, are desired when modelling ecological systems, where it is common for accurate and detailed information on functional form and parameters to be unavailable. Previous work by Jahedi et al. [Robustness of solutions of almost every system of equations, SIAM J. Appl. Math. 82(5) (2022), pp. 1791–1807; Structured systems of nonlinear equations, SIAM J. Appl. Math. 83(4) (2023), pp. 1696–1716.] has established criteria to imply the prevalence of robust steady states for systems with minimal predetermined structure, including conventional structured systems. We review that work and extend it by allowing symmetries in the system structure, which present added obstructions to robustness.

Migratory divides separate populations of migratory animals, facilitating the evolution of intraspecific differences in migration strategies. Migration strategies are expected to be different for birds using different flyways and environments, but the knowledge regarding the impact of the flyway on individual migration strategies is scarce. By using satellite tracking and neckband resightings, we reveal the existence and structure of a gradual migratory divide between two European flyway populations of greylag geese Anser anser. Birds breeding at the far end of the Gulf of Bothnia in the Baltic Sea coast use the Western Flyway, those breeding in the Gulf of Finland the Central Flyway and those breeding between these extremes scatter to the two flyways. By using Gaussian process modelling, we show that migration strategies differed between the flyways. The birds using the Western Flyway migrated earlier in autumn, performed longer annual migration and made a clear stopover during migration, whereas the birds using the Central Flyway flew directly to their wintering sites. The gradual migratory divide that also divides migration strategies provides insights into migratory divides on birds with learned migration. Distinct migration strategies in different flyways provide exciting possibilities to further study the factors driving migration strategies. [ABSTRACT FROM AUTHOR]

Dynamic Global Vegetation Models (DGVMs) provide a state‐of‐the‐art process‐based approach to study the complex interplay between vegetation and its physical environment. For example, they help to predict how terrestrial plants interact with climate, soils, disturbance and competition for resources. We argue that there is untapped potential for the use of DGVMs in ecological and ecophysiological research. One fundamental barrier to realize this potential is that many researchers with relevant expertize (ecology, plant physiology, soil science, etc.) lack access to the technical resources or awareness of the research potential of DGVMs. Here we present the Land Sites Platform (LSP): new software that facilitates single‐site simulations with the Functionally Assembled Terrestrial Ecosystem Simulator, an advanced DGVM coupled with the Community Land Model. The LSP includes a Graphical User Interface and an Application Programming Interface, which improve the user experience and lower the technical thresholds for installing these model architectures and setting up model experiments. The software is distributed via version‐controlled containers; researchers and students can run simulations directly on their personal computers or servers, with relatively low hardware requirements, and on different operating systems. Version 1.0 of the LSP supports site‐level simulations. We provide input data for 20 established geo‐ecological observation sites in Norway and workflows to add generic sites from public global datasets. The LSP makes standard model experiments with default data easily achievable (e.g., for educational or introductory purposes) while retaining flexibility for more advanced scientific uses. We further provide tools to visualize the model input and output, including simple examples to relate predictions to local observations. The LSP improves access to land surface and DGVM modelling as a building block of community cyberinfrastructure that may inspire new avenues for mechanistic ecosystem research across disciplines. [ABSTRACT FROM AUTHOR]

Projections of climate change impacts on marine ecosystems have revealed long-term declines in global marine animal biomass and unevenly distributed impacts on fisheries. Here we apply an enhanced suite of global marine ecosystem models from the Fisheries and Marine Ecosystem Model Intercomparison Project (Fish-MIP), forced by new-generation Earth system model outputs from Phase 6 of the Coupled Model Intercomparison Project (CMIP6), to provide insights into how projected climate change will affect future ocean ecosystems. Compared with the previous generation CMIP5-forced Fish-MIP ensemble, the new ensemble ecosystem simulations show a greater decline in mean global ocean animal biomass under both strong-mitigation and high-emissions scenarios due to elevated warming, despite greater uncertainty in net primary production in the high-emissions scenario. Regional shifts in the direction of biomass changes highlight the continued and urgent need to reduce uncertainty in the projected responses of marine ecosystems to climate change to help support adaptation planning.

Bahlburg et al. re-implemented eight growth models of Antarctic krill and showed that their predictions are all over the place. The authors discuss the reasons for this and how more coherence in modelling could be achieved through systematic model comparison and integration. For this, we need a common language. [ABSTRACT FROM AUTHOR]

Aim: The centre‐periphery hypothesis (CPH) explains the decline of species abundance towards range limits and how this is driven by increasing ecological marginality. So far, most studies testing the CPH have focused on abiotic factors contributing to marginality, while the role of biotic interactions in limiting species distribution has been neglected. Here, we investigate both drivers' roles in restricting an orchid's range along a broad environmental gradient. Location: Atlantic Forest and Pampas grasslands (south and southeastern Brazil). Taxa: Sand dune orchid Epidendrum fulgens (Orchidaceae). Methods: We integrated empirical data on geographical distribution, pollinator richness and genetic diversity along the entire range of the species to investigate whether range limits match niche limits and whether habitat suitability declines towards low‐ and high‐latitude species ranges. We performed niche models to predict niche limits and used polynomial and linear regression models to investigate the associations between ecological niche and species range as well as to test the relationship between genetic‐derived metrics and the geographical and ecological distances. Results: Ecological conditions become more marginal towards the edges of the E. fulgens range, with an abrupt variation in precipitation. While pollinator richness increases habitat suitability of E. fulgens in the low‐latitude edge range, climate has primarily shaped the species' high‐latitude limit. Genetic diversity within populations decreases, while genetic differentiation increases towards both margins, although with a more consistent pattern for the low‐latitudinal component. Main Conclusions: This study corroborates the predictions of CPH regarding ecological and genetic patterns of variation in space and highlights distinct factors limiting geographical distribution at the opposite margins of a latitudinal and narrowly distributed species. This improves our understanding on how biotic and abiotic variables limit species distribution ranges along latitudinal gradients in an extremely diverse and vulnerable tropical ecosystem, with potential for informing conservation practices. [ABSTRACT FROM AUTHOR]

Background: The Black Sea is one of the most anthropogenically disturbed marine ecosystems in the world because of introduced species, fisheries overexploitation, nutrient enrichment via pollution through river discharge, and the impacts of climate change. It has undergone significant ecosystem transformations since the 1960s. The infamous anchovy and alien warty comb jelly Mnemiopsis leidyi shift that occurred in 1989 is the most well-known example of the drastic extent of anthropogenic disturbance in the Black Sea. Although a vast body of literature exists on the Black Sea ecosystem, a holistic look at the multidecadal changes in the Black Sea ecosystem using an ecosystem- and ecology-based approach is still lacking. Hence, this work is dedicated to filling this gap. Methods: First, a dynamic food web model of the Black Sea extending from 1960 to 1999 was established and validated against time-series data. Next, an ecological network analysis was performed to calculate the time series of synthetic ecological indicators, and a regime shift analysis was performed on the time series of indicators. Results: The model successfully replicated the regime shifts observed in the Black Sea. The results showed that the Black Sea ecosystem experienced four regime shifts and was reorganized due to effects instigated by overfishing in the 1960s, eutrophication and establishment of trophic dead-end organisms in the 1970s, and overfishing and intensifying interspecies trophic competition by the overpopulation of some r-selected organisms (i.e., jellyfish species) in the 1980s. Overall, these changes acted concomitantly to erode the structure and function of the ecosystem by manipulating the food web to reorganize itself through the introduction and selective removal of organisms and eutrophication. Basin-wide, cross-national management efforts, especially with regard to pollution and fisheries, could have prevented the undesirable changes observed in the Black Sea ecosystem and should be immediately employed for management practices in the basin to prevent such drastic ecosystem fluctuations in the future. [ABSTRACT FROM AUTHOR]

The dandelions from Taraxacum sect. Erythrosperma are taxonomically well distinguished and ecologically restricted to warm and sunlit habitats of steppes, dry and sandy grasslands, and distributed in temperate regions of Europe and Central Asia, with some being introduced to North America. Despite the long tradition of botanical research, the taxonomy and distribution of dandelions of T. sect. Erythrosperma is still underexplored in central Europe. In this paper, by combining traditional taxonomic studies supported by micromorphological, molecular and flow cytometry analyses as well as potential distribution modelling we shed light on taxonomical and phylogenetical relationships between members of T. sect. Erythrosperma in Poland. We also provide an identification key, species-checklist, detailed descriptions of morphology and occupated habitats as well as distribution maps for 14 Polish erythrosperms (T. bellicum, T. brachyglossum, T. cristatum, T. danubium, T. disseminatum, T. dissimile, T. lacistophyllum, T. parnassicum, T. plumbeum, T. proximum, T. sandomiriense, T. scanicum, T. tenuilobum, T. tortilobum). Finally, conservation assessments performed using the IUCN method and threat categories for all the examined species are proposed. [ABSTRACT FROM AUTHOR]

Forests provide a wide variety of ecosystem services (ES) to society. The boreal biome is experiencing the highest rates of warming on the planet and increasing demand for forest products. To foresee how to maximize the adaptation of boreal forests to future warmer conditions and growing demands of forest products, we need a better understanding of the relative importance of forest management and climate change on the supply of ecosystem services. Here, using Finland as a boreal forest case study, we assessed the potential supply of a wide range of ES (timber, bilberry, cowberry, mushrooms, carbon storage, scenic beauty, species habitat availability and deadwood) given seven management regimes and four climate change scenarios. We used the forest simulator SIMO to project forest dynamics for 100 years into the future (2016–2116) and estimate the potential supply of each service using published models. Then, we tested the relative importance of management and climate change as drivers of the future supply of these services using generalized linear mixed models. Our results show that the effects of management on the future supply of these ES were, on average, 11 times higher than the effects of climate change across all services, but greatly differed among them (from 0.53 to 24 times higher for timber and cowberry, respectively). Notably, the importance of these drivers substantially differed among biogeographical zones within the boreal biome. The effects of climate change were 1.6 times higher in northern Finland than in southern Finland, whereas the effects of management were the opposite—they were three times higher in the south compared to the north. We conclude that new guidelines for adapting forests to global change should account for regional differences and the variation in the effects of climate change and management on different forest ES. [ABSTRACT FROM AUTHOR]

There is an urgent need to analyse and understand small-scale fisheries environment under biotic and abiotic stressors. In this work, we use a kelp forest ecosystem in Baja California, Mexico to present a novel approach, comparing two network models based on different information sources. First, we developed a conventional scientific knowledge network model (CSK) parameterized with in-situ observations. Second, we used a local ecological knowledge network model (LEK) based on interviews with local fishers. Our main objectives were: (a) verify if the two knowledge sources generated comparable models, and (b) explore model responses to disturbance scenarios. The CSK model presented greater detail at lower trophic levels, contrary to the LEK model. Additionally, we simulated top-down and bottom-up ecological disturbances. With a top-down disturbance, the groups' abundance increased following a cascade effect whereas, in the bottom-up disturbance, changes did not transfer uniformly. We also simulated anthropogenic disturbances through fishing pressure on three target species (lobsters, sea urchins, and sea bass). Our findings show similar patterns with the highest degree of change when lobsters are removed. Our findings highlight the potential of model complementarity and support the relevance of ecological network models to navigate future climate and anthropogenic uncertainty. [ABSTRACT FROM AUTHOR]

The Oligocene and Miocene tectonic and biogeographical alterations of the peri-Mediterranean area could strongly impact the speciation processes and migrations of Mediterranean sandfly species. To understand the possible former role of this palaeobiogeographic factor on ancestral sandfly species, the potential suitability values of five Larroussius, two Paraphlebotomus and one Phlebotomus species were modelled from the Rupelian to the Tortonian stage in the Central Paratethys. The bioclimatic extrema of this sandfly species and the Coexistence Approach-based palaeoclimatic reconstructions made the basis of hypothesis testing. In the case of the Tortonian stage, a georeferenced climatic model was created. The models indicate that the suitability values could notably vary by species and periods. The monsoon-influenced humid subtropical climate of the Oligocene epoch could be less suitable for the ancestors of Mediterranean sandfly taxa than the later, drier humid subtropical climates-characterised Miocene stages. The Central Paratethys area could be less suitable for ancestors of the extant Paraphlebotomus, and Phlebotomus in the Miocene epoch compared to Larroussius species. It may indicate that the Central Paratethys formed a barrier against the east-to-west migration of the ancestors of Phlebotomus and Paraphlebotomus species. In contrast, Larroussius species could prefer the Miocene climate of the Central Paratethys. In the Tortonian stage, mainly the coastal areas of the sea could be colonised by sandflies. These results indicate that the coasts of the Central Paratethys should be not only considered as a potential former migration route but could be a part of the speciation area of Larroussius sandflies. [ABSTRACT FROM AUTHOR]

Japanese encephalitis virus (JEV) is an arboviral, encephalitogenic, zoonotic flavivirus characterized by its complex epidemiology whose transmission cycle involves reservoir and amplifying hosts, competent vector species and optimal environmental conditions. Although typically endemic in Asia and parts of the Pacific Islands, unprecedented outbreaks in both humans and domestic pigs in southeastern Australia emphasize the virus' expanding geographical range. To estimate areas at highest risk of JEV transmission in Australia, ecological niche models of vectors and waterbirds, a sample of piggery coordinates and feral pig population density models were combined using mathematical and geospatial mapping techniques. These results highlight that both coastal and inland regions across the continent are estimated to have varying risks of enzootic and/or epidemic JEV transmission. We recommend increased surveillance of waterbirds, feral pigs and mosquito populations in areas where domestic pigs and human populations are present. [ABSTRACT FROM AUTHOR]

Prunus salicina Lindl. (P. salicina) is an essential cash crop in China, and brown rot (BR) is one of its most important diseases. In this study, we collected geographic location information on P. salicina and Monilinia fructicola (G. Winter) Honey (M. fructicola), one of the BR pathogenic species, and applied the MaxEnt model to simulate its potential suitable distribution in China. There have been discussions about the dominant environmental variables restricting its geographical distribution and their overlap. The results showed that the mean temperature of the coldest quarter, precipitation of the warmest quarter, precipitation in July, and minimum temperatures in January and November were the main climatic variables affecting the potential distribution of P. salicina, while the coldest quarter, precipitation of the driest month, precipitation of March, precipitation of October, maximum temperatures of February, October, and November, and minimum temperature of January were related to the location of M. fructicola. Southern China had suitable conditions for both P. salicina and M. fructicola. Notably, the overlap area of P. salicina and M. fructicola was primarily located southeast of 91°48′ E 27°38′ N to 126°47′ E 41°45′ N. The potential overlap area predicted by our research provided theoretical evidence for the prevention of BR during plum planting. [ABSTRACT FROM AUTHOR]

Background The Black Sea is one of the most anthropogenically disturbed marine ecosystems in the world because of introduced species, fisheries overexploitation, nutrient enrichment via pollution through river discharge, and the impacts of climate change. It has undergone significant ecosystem transformations since the 1960s. The infamous anchovy and alien warty comb jelly Mnemiopsis leidyi shift that occurred in 1989 is the most well-known example of the drastic extent of anthropogenic disturbance in the Black Sea. Although a vast body of literature exists on the Black Sea ecosystem, a holistic look at the multidecadal changes in the Black Sea ecosystem using an ecosystem- and ecology-based approach is still lacking. Hence, this work is dedicated to filling this gap. Methods First, a dynamic food web model of the Black Sea extending from 1960 to 1999 was established and validated against time-series data. Next, an ecological network analysis was performed to calculate the time series of synthetic ecological indicators, and a regime shift analysis was performed on the time series of indicators. Results The model successfully replicated the regime shifts observed in the Black Sea. The results showed that the Black Sea ecosystem experienced four regime shifts and was reorganized due to effects instigated by overfishing in the 1960s, eutrophication and establishment of trophic dead-end organisms in the 1970s, and overfishing and intensifying interspecies trophic competition by the overpopulation of some r-selected organisms (i.e., jellyfish species) in the 1980s. Overall, these changes acted concomitantly to erode the structure and function of the ecosystem by manipulating the food web to reorganize itself through the introduction and selective removal of organisms and eutrophication. Basin-wide, cross-national management efforts, especially with regard to pollution and fisheries, could have prevented the undesirable changes observed in the Black Sea ecosystem and should be immediately employed for management practices in the basin to prevent such drastic ecosystem fluctuations in the future.

The steep rise of infections caused by bacteria that are resistant to antimicrobial agents threatens global health. However, the association between antimicrobial use and the prevalence of resistance is not straightforward. Therefore, it is necessary to quantify the importance of additional factors that affect this relationship. We theoretically explore how the prevalence of resistance is affected by the combination of three factors: antimicrobial use, bacterial transmission, and fitness cost of resistance. We present a model that combines within-host, between-hosts and between-populations dynamics, built upon the competitive Lotka-Volterra equations. We developed the model in a manner that allows future experimental validation of the findings with single isolates in the laboratory. Each host may carry two strains (susceptible and resistant) that represent the host’s commensal microbiome and are not the target of the antimicrobial treatment. The model simulates a population of hosts who are treated periodically with antibiotics and transmit bacteria to each other. We show that bacterial transmission results in strain co-existence. Transmission disseminates resistant bacteria in the population, increasing the levels of resistance. Counterintuitively, when the cost of resistance is low, high transmission frequencies reduce resistance prevalence. Transmission between host populations leads to more similar resistance levels, increasing the susceptibility of the population with higher antimicrobial use. Overall, our results indicate that the interplay between bacterial transmission and strain fitness affects the prevalence of resistance in a non-linear way. We then place our results within the context of ecological theory, particularly on temporal niche partitioning and metapopulation rescue, and we formulate testable experimental predictions for future research.

Land surface phenology (LSP), the study of the seasonal vegetation dynamics from remote sensing imagery, provides crucial information for plant monitoring and reflects the responses of ecosystems to climate change. The Moderate Resolution Imaging Spectroradiometer (MODIS) phenology product (MCD12Q2) provides global LSP information, but it has large spatial gaps in many regions, especially in ecosystems where rainfall influences phenology more than temperature. This study aimed to improve spatial coverage of LSP retrieval in these ecosystems. To do so, we used a regionally modified threshold algorithm for LSP retrievals, which were tested over continental Australia as it includes diverse landscapes of arid, mesic, and forest environments. We generated LSP metrics annually from 2003 to 2018 using satellite Enhanced Vegetation Index (EVI) time series at 500 m resolution, including the start, peak, end, and length of growing seasons, the minimum EVI value prior to and after the peak date, the seasonal maximum EVI value, the integral EVI value during the growing season (an approximation of productivity), and seasonal amplitude (maximum EVI value minus minimum EVI). Our regionally optimised algorithm improved the spatial coverage of LSP information in Australia from only 26 % of the continent to 70 % averaged across 16 years. Our results showed that the growing season amplitude was low (EVI

Models provide scientists with knowledge about target systems. An important group of models are those that are called general. However, what exactly is meant by generality in this context is somewhat unclear. The aim of this paper is to draw out a distinction between two notions of generality that has implications for scientific practice. Some models are general in the sense that they apply to many systems in the world and have many particular targets. Another sense is captured by models that are aimed at understanding the fundamental or underlying dynamics of a phenomenon, as opposed to how it manifests in each particular case. They have non-specific, i.e. generic targets. While both notions of generality and genericness are legitimate and correspond to different aspects of scientific practice, they must be distinguished. Failing to do so obscures the danger of overgeneralisation faced by general models and facilitates the illegitimate use of generic models as general models. This can lead to a reduction of the explanatory and predictive power of both. [ABSTRACT FROM AUTHOR]

Early detection is important for the management of invasive alien species. In the last decade citizen science has become an important source of such data. Here, we used opportunistic records from the "LIFE ARTEMIS" citizen science project, in which people submitted records from places where they observed tree pests, to understand the distribution of a rapidly-spreading forest pest: the oak lace bug (Corythucha arcuata) in Slovenia. These citizen science records were not distributed randomly. We constructed a species distribution model for C. arcuata that accounted for the biased distribution of citizen science by using the records of other tree pests and diseases from the same project as pseudo-absences (so-called constrained pseudo-absences), and compared this to a model with pseudo-absences selected randomly from across Slovenia. We found that the constrained pseudo-absence model showed that C. arcuata was more likely to be found in east, in places with more oak trees and at lower elevations, and also closer to highways and railways, indicating introduction and dispersal by accidental human transport. The outputs from the model with random pseudo-absences were broadly similar, although estimates from this model tended to be higher and less precise, and some factors that were significant (proximity to minor roads and human settlements) were artefacts of recorder bias, showing the importance of taking the distribution of recording into account wherever possible. The finding that C. arcuata is more likely to be found near highways allows us to design advice for where future citizen science should be directed for efficient early detection. [ABSTRACT FROM AUTHOR]

• Good modelling practices ensure scientific workflows are accessible and reusable. • Yet, reproducibility in ecological modelling remains low, lacking functional code. • We identify three key factors inherent to our academic structure hindering GMP. • Lack of specific training, of GMP in research budgets, and of academic recognition. • Challenging academia's status quo fosters an environment where GMP are expected. Implementing good modelling practices (GMP) in ecological sciences is key to improving scientific reliability. Despite the increased availability of guidelines and protocols detailing how principles such as FAIR and PERFICT can be implemented to improve good modelling practices, the sharing of code which can reproduce results and workflows remains remarkably low. In this work, we explore potential root causes of this discrepancy. We identify three key factors inherent to the current academic structure that, in our experience, might play a role in hindering a wider adoption of GMP: (1) acknowledgment of the time required to implement GMP in projects, (2) the lack of GMP and software development training among ecologists, and (3) perception of GMP as unrewarding in the short-term. We argue that there is an urgent need for systemic changes. Such changes include (1) a cultural shift to value the incorporation of GMP across projects, emphasising the need for explicit budget allocation and careful scheduling of its implementation, (2) redesigning academic curricula to explicitly include GMP and software development as fundamental disciplines in ecology, and (3) an increase in recognition of open and functional code and workflows for career advancement. We call for concerted efforts for bridging this gap, and propose a hopeful outlook emphasising the role of a new generation of scientists and tools committed to good science. Proposing concrete actions, we aim to start a discussion on challenging academia's status quo in ecology and support scientists in bringing a significant paradigm shift to ecological modelling. [ABSTRACT FROM AUTHOR]

• The Coastal Generalized Ecosystem Model (CGEM) is an open-source model. • CGEM offers different formulations for rate processes, providing a flexible model structure. • Model formulations have significant effects on simulation outcomes. • Flexible model structure is important for adapting models to different ecosystems. • Formulations impact simulation outcomes for climate change and HABs. The Coastal Generalized Ecosystem Model (CGEM) is a biogeochemical model developed to study regulating processes of water-column optical properties, water-column and benthic carbon, oxygen, and nutrient cycles, and phytoplankton and zooplankton dynamics. CGEM offers numerous formulations for important rate processes, providing users flexibility in altering model structure. This flexibility also provides a means for evaluating model structural uncertainty and impacts on simulations, which are rarely evaluated with numerical ecosystem models. As an open-source model, CGEM also offers users the option to implement new formulations or modify existing routines. We also provide a full description of the model formulations, state variables, and model parameters in CGEM. Using two published case studies, we explore how different formulations for light attenuation, phytoplankton temperature growth response, and sediment processes impact simulations. We discuss CGEM's role as a new ecosystem model within the modeling community and opportunities to address current and future water quality issues. [ABSTRACT FROM AUTHOR]