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In this study, we analyzed the characteristics of the most cited papers regarding species distribution predictive models (SDPMs). We found 173 papers on SDPMs that received at least 100 citations until 2013, according to the Thomson Reuters Web of Science database. These papers were published between 1991 and 2012, with the majority published between 2002 and 2012, indicating the rapid development of this field of research. The papers were published mainly in journals listed in the ecology category on the Web of Science. Almost half of the top-cited papers were methodological, introducing novel modeling methods and software. Applied papers on species conservation and biodiversity management, climate change, phylogeography, and biosecurity also figured out among the top-cited papers. Researchers from 174 institutions in 27 countries, with 51% of the papers being internationally collaborative and 69% inter-institutionally collaborative, published the papers. Among all 173 papers, seven papers stood out as having a great impact on the field, receiving more than 1000 citations each. Finally, the results found by analyzing the top-cited SDPMs papers support the view of a growing interest and rapid development of this research field over the past two decades. The top-cited papers primarily focused on the development and evaluation of novel methods to improve the performance of the models, and thus, to better predict the environmental suitability for species in applied studies. [ABSTRACT FROM AUTHOR]

Stochastic cellular automata for rock-paper-scissors games are related to Lotka-Volterra model. Simulations are usually performed by two methods local and global interactions. It is well known that the population dynamics with local interaction is stable, where all species coexist. In contrast, global interaction leads to extinction. So far, theories such as mean-field theory and pair approximation have been presented, but they never explained the stable dynamics in local simulation. In the present article, we apply effective medium approximation (EMA) which has been developed in Physics. The effective medium is determined in a self-consistent way. The EMA theory well predicts the stability of population dynamics. Moreover, it fairly explains the aggregation of each species observed in the stationary state. [ABSTRACT FROM AUTHOR]

Processes occurring within small areas (patch-scale) that influence species richness and spatial heterogeneity of larger areas (landscape-scale) have long been an interest of ecologists. This research focused on the role of patch-scale deterministic chaos arising in phytoplankton assemblages characteristic of “Rock-Paper-Scissors” population dynamics (i.e., competitively non-hierarchical). We employed a simple 2-patch model configuration with lateral mixing and through-flow, and tested the robustness of species richness at the scale of the landscape and spatial heterogeneity. Three different assemblages were used that in a dimensionless box model configuration exhibited chaotic behavior. Our results showed that when a spatial dimension was added to the model configuration, and when all species were shared between patches (i.e., no invading populations), chaos-induced species richness and spatial heterogeneity were quickly reduced with the onset of mixing. While assemblages in each patch were comprised of exactly the same species, they differed in their proportional population densities due to differing stages of succession and the incidence of alternative assemblage structures. Even at very low mixing rates (0.001d−1), which produced low passive migration rates (0.1% of the total biomass per day), the incidence of high richness and heterogeneity decreased by ∼80%. Interestingly, this sensitivity was not the same for the three assemblages tested. Declines in species richness and spatial heterogeneity associated with mixing were greater in assemblages comprised of competitively dissimilar species (based on the area occupied in the resource-tradeoff space defined by the R* model). The underlying mechanisms may involve the degree to which nutrient dynamics are altered with the arrival of immigrants. Our findings suggest that in partially to well-mixed aquatic systems, the roles of patch-scale non-hierarchical competition and chaos as factors maintaining species richness and spatial heterogeneity may be limited. However, in aquatic systems that experience periods of very low mixing, or even disconnection, non-hierarchical competition and chaos might indeed contribute significantly to biodiversity. [Copyright &y& Elsevier]

In the past 30 years, the notion of landscape has emerged in ecology as a result of both theoretical strategies and practical aspects of land use. This has generated a variety of computerized models addressing both objectives and techniques. Scientists model landscapes for at least two reasons: to better understand the landscape dynamics themselves (called intrinsic needs) and to offer a realistic frame to support other ecological processes (extrinsic needs). This special issue concerns both needs and illustrates the way socioeconomic and/or ecological mechanisms of various landscapes have been understood through modelling approaches. It outlines the links between landscape and model concepts, focusing on one hand on several landscape types (agricultural, forested and aquatic) and on the other hand on several landscape model characteristics (explicit or neutral, dynamic or static, patchy or continuous and multi- or mono-scale). The patterns and processes of each landscape model presented in this issue, in particular, should be analysed in order to highlight the way they are contributing to the landscape ecology discipline. We finally argue that the discipline can now offer a theoretical dimension to landscape dynamics, aiming at understanding the possible mechanism unity underlying this complex object. [Copyright &y& Elsevier]

Abstract: In agricultural soil, a suite of anthropogenic events shape the ecosystem processes and populations. However, the impact from anthropogenic sources on the soil environment is almost exclusively assessed for chemicals, although other factors like crop and tillage practices have an important impact as well. Thus, the farming system as a whole should be evaluated and ranked according to its environmental benefits and impacts. Our starting point is a data set describing agricultural events and soil biological parameters. Using machine learning methods for inducing regression and model trees, we produce empirical models able to predict the soil quality from agricultural measures in terms of quantities describing the soil microarthropod community. We are also interested in discovering additional higher level knowledge. In particular, we have identified the most important factors influencing the population densities of springtails and mites and their biodiversity. We also identify to which agricultural actions different microarthropods react distinctly. To obtain this higher level knowledge, we employ multi-objective regression trees. [Copyright &y& Elsevier]

Abstract: Soil water and temperature regimes in the tropical moist forest on Barro Colorado Island, Panama, were simulated directly from meteorological data using the model SWEAT. Separate field observations from root-exclusion, litter-removal and control treatments in one small and one large forest gap were used for calibration and validation. After irrigating all treatments to field capacity, soil matric potential and temperature were measured over 17 days at four depths ≤50mm using the filter-paper technique and bead thermistors. Understorey environments were also simulated under the same initial conditions. The results suggest that three distinct scenarios, controlled by gap size, describe how the above- and below-ground processes controlling soil drying are coupled: (1) in the large gap, root water extraction by surrounding trees is negligible so soil drying is dominated by evaporation from the soil surface. Soil temperature is dominated by direct solar heating and cooling due to evaporation. (2) In the small gap, root water extraction dominates soil drying with soil evaporation playing a minor role. Soil temperature is still dominated by direct sunlight with some cooling due to evaporation. (3) In the understorey, root water extraction dominates soil drying. Soil temperature is dominated by heat conduction from deep soil layers with some evaporation and sensible heat transfer. The contrasting soil drying regimes imposed by variation in canopy structure enhance micro-environmental heterogeneity and the scope for differential germination and seedling establishment in coexisting tropical tree species. [Copyright &y& Elsevier]

Abstract: Although the ecological risks of toxic chemicals are usually assessed on the basis of individual responses, such as survival, reproduction or growth, ecotoxicologists are now attempting to assess the impact of environmental pollution on the dynamics of naturally exposed populations. The main issue is how to infer the likely impact on the population of the toxic effects observed at the individual level. Dynamic energy budget in toxicology (DEBtox) is the most user-friendly software currently available to analyze the experimental data obtained in toxicity tests performed on individuals. Because toxic effects are diverse and because the sensitivity of individuals varies considerably depending on life-cycle stage, Leslie models offer a convenient way of predicting toxicant effects on population dynamics. In the present study, we first show how parameter inputs, estimated from individual data using DEBtox, can be coupled using a Leslie matrix population model. Then, using experimental data obtained with Chironomus riparius, we show how the effects of a pesticide (methiocarb) on the population growth rate of a laboratory population can be estimated. Lastly, we perform a complex sensitivity analysis to pinpoint critical age classes within the population for the purposes of the field management of populations. [Copyright &y& Elsevier]

Ecological modelling often involves addressing challenges such as dependence in responses, e.g., spatial and/or temporal correlation, heterogeneity of variance, and hierarchical structures inherent in ecological processes and data. A constant challenge is the inadequacy of the data to well address the questions of interest. What is observable may not be sufficiently informative. What has been observed may not have been well designed. Carefully conceived modelling can take us to improved inference compared with adopting standard inference tools like basic regression and analysis of variance. Hierarchical modelling techniques provide a powerful framework for capturing these complexities by explicitly modelling the multi-level structure of ecological systems. In this paper we focus on good modelling practice in the hierarchical Bayesian framework. We discuss good modelling practice in the form of 10 steps, elaborated with a running example, to aid quantitative ecologists interested in employing Bayesian modelling. Two of the authors are statisticians who view themselves primarily as stochastic modellers. The other two are quantitative ecologists who view such stochastic tools as essential for advancing ecological knowledge. Hence, implicitly, we pay careful attention to good modelling practice. Further, we argue the benefits that accrue to working in a Bayesian framework as the paper is developed. It is worth noting that the steps we propose, though presented in the context of ecological modelling, are appropriate for effective model building across most fields of application. [ABSTRACT FROM AUTHOR]

• This paper emphasizes on the necessity for a robust connection between science and practice in socio-environmental systems research. • Adopting DSR could bridge theory and practice gaps in SES research. • DSR application in case studies shows tailored solutions for timely implementation. • DSR, complementing traditional methods, enhances integration of practical studies and local knowledge into academia. A strong and close connection between science and practice in socio-environmental systems (SES) research and modelling is warranted to face complex and interdisciplinary socio-environmental challenges around issues such as sustainability and climate change. However, significant gaps and inadequate knowledge flow between the scientific and practical aspects of SES exist. This paper highlights several areas that require improvement, including reducing the lag time between scientific solutions and practical implementation, making academic research more relevant to practitioners and decision makers, enhancing the transfer and translation of scientific outputs into practice, improving the integration of practical studies and local knowledge into academia, and addressing the complexity of real-world problems more effectively. To bridge these gaps, we advocate for adopting a design science research (DSR) approach in socio-environmental research. DSR is a problem-solving paradigm that creates applied artifacts such as models, methods, and design theories to enrich knowledge and provide practical solutions. By applying DSR, we can reduce the time between identifying a problem and implementing a solution while facilitating the transfer and translation of research findings into practical applications. We demonstrate the value of artifacts in extracting solutions from practical studies and local knowledge and making them applicable to a broader range of socio-environmental research. DSR emphasises the design of solutions and learning about complex problems through the process of solving them. We demonstrate the merits of the DSR approach application in SES through two case studies to provide a first trial of DSR's practicality, value and challenges. Although DSR is not yet widely recognised or applied in socio-environmental research, this paper encourages its adoption as an overarching approach that complements traditional methods in SES research. By strengthening the connection between practice and science, DSR has the potential to address the existing gaps and improve the effectiveness of socio-environmental research and modelling. The paper therefore advocates that, for complex SES problems, DSR offers ways to not only strengthen but also escalate the overall value of SES modelling and practice to addressing earth's grand challenge problems. It contributes directly to the Joint Special Issue on good modelling practices, including developing Findable, Accessible, Interoperable and Reusable (FAIR) artifacts. [ABSTRACT FROM AUTHOR]

Computing models are a good and effective way to study population dynamics of endangered species like giant pandas. Until now, a variety of computing models were proposed for giant pandas, but no survey on computing models for population dynamics of giant panda ecosystems has yet appeared in the specialised literature. It is necessary to provide an overview of the state-of-the-art of this topic so as to allow newcomers to the area to obtain a clear understanding of developments, key research problems, properties of computing models in this field, including those that are currently under way. This paper proposes a unified framework to clearly summarise the computing models used for studying the population dynamics of threatened species with respect to theoretical and application aspects and presents a comprehensive and systematic survey of the state-of-the-art computing models. This paper also introduces basic concepts of computing models, surveys their theoretical developments and applications, sketches the differences between various computing model variants, and compares the advantages and limitations of the models. Comparing with single-factor computing models and double-factor computing models, multi-factor computing models, especially multi-environment population dynamics P systems, are more suitable for investigating giant panda ecosystems, because the use of bottom-up way to consider evolutionary behaviours influencing giant pandas' population. • Computing models for population dynamics of giant pandas. • Unified framework of computing models for giant pandas. • Comprehensive and systematic survey of the state-of-the-art computing models. • Differences, advantages and limitations of computing models. [Display omitted] [ABSTRACT FROM AUTHOR]

• Process-based environmental fate model (PESTIPOND) for dissolved pesticides is developed & tested. • PESTIPOND integrates a hydrological sub-model and the key transport, transfer, and transformation processes of dissolved pesticides in APs. • Sensitivity analysis underlines that sorption, biotransformation, and photolysis are the governing processes behind pesticide fate in APs. • PESTIPOND is designed to be integrated into surface water assessment tools to predict pesticide behavior at the watershed scale. Some artificial ponds (APs) are designed to collect part of the agricultural water fluxes and dissipate their pesticide contamination through a synergy of physicochemical processes. As part of a two-paper series, this paper describes the development of PESTIPOND, a process-based model for pesticide fate in AP. PESTIPOND comprises a hydrological model and a pesticide transfer model. A test-case simulation shows that PESTIPOND can predict the mass dynamics of different pesticides within AP compartments. Sensitivity analysis evidenced that adsorption–desorption, biotransformation, and photolysis have the most significant impact on the behavior of dissolved pesticides in APs. PESTIPOND is a promising tool for better understanding the performance of ponds and can help improve AP's efficiency in preserving water quality. A second paper addresses the application of PESTIPOND in a real-case scenario and its validation (Bahi et al., 2023a). The model can thus be integrated into landscape modeling tools (e.g., surface water assessment tools) to predict pesticide transfer at the watershed scale. [ABSTRACT FROM AUTHOR]

• Time-dependent environmental fate model (PESTIPOND) for dissolved pesticides is developed and validated. • Model accurately predicts concentrations after episodic pesticide applications. • Model quantifies the mass partition of dissolved pesticides in the water and sediments of ponds. • Model estimates the contribution of each key process behind pesticide dissipation in artificial ponds. • PESTIPOND is freely available for use and further study. Some artificial ponds (APs) are designed to collect part of the agricultural water fluxes and dissipate pesticide contamination through a synergy of physicochemical processes. APs act as buffer zones and mitigate pesticide transfer of farm plots to natural water resources. As part of a two-paper series, this paper addresses the application and validation of the PESTIPOND model. PESTIPOND is a process-based model developed to predict pesticides' behavior and distribution in APs located in drained agricultural catchments. Paper I (Bahi et al., 2023a) describes the development and sensitivity analysis of the model. PESTIPOND was applied on the Rampillon AP to characterize the fate of seven different pesticides and five monitoring periods while considering the key transfer and transformation processes. The model was assessed through various methods against the observed data in simulating pesticide dynamics. The statistical and graphical evaluation of PESTIPOND reflected a good performance except for boscalid. The sensitivity analysis and application of the model evidenced that adsorption-desorption and biotransformation in the pond water are major processes behind pesticide dissipation. Hydrophobic and lowly mobile pesticides are more likely to be bio-transformed at the water-sediment interface. This work highlights the link between the hydraulic residential time (HRT), temperature, and APs' efficiency in minimizing pesticide transfer into the environment. The model predicted that the actual efficiency of the AP covering 0.15% of the drained catchment would double if the pond's surface area covered at least 1% of the catchment. Moreover, the model's predictions evidenced that a temperature rise of 10 °C will increase the dissipation of pesticides by only 8%. PESTIPOND provides key elements that are useful to design and manage ponds with optimal efficiency. Hence, these APs can be complementary solutions to pesticide use regulation to reduce the transfer of agricultural contamination into freshwater resources. [ABSTRACT FROM AUTHOR]

• This paper proposes an Indexed Meta-Goal Planning Model to solve imprecision in data. • This integrated model is a novelty and has been applied to sustainable optimal cropping patterns. • It has been tested in Iran to solve conflicting uses of natural resources. • The results compared with Sen and Pal show no variation in land allocation for each crop. • the least variation in all problems is for wheat and barley production. Meta-Goal Programming (MGP) is a simultaneous cognitive evaluation of the degree of achievements for original decision goals considered in a GP model. However, in most real-world situations, environmental coefficients and related parameters are not easily available. In such a situation, the decision-maker must consider various conflicting targets in a framework of uncertain aspiration levels at the same time. On the other side, Interval Programming (IP) is a method used to increase the range of available decision-maker preference structures in GP. In the perspective of solving the conflicts between agriculture and water use towards sustainability, this paper proposes an Interval Meta-Goal Programming Model (IMGPM) dealing with imprecision in data that covers interval coefficients, target intervals, and interval bounds of meta-goals. This novel methodology has been tested in a study area in Iran to validate its added value in solving conflicting uses of natural resources by economic sectors. This integration together with its application for sustainable optimal cropping patterns (agroecosystem planning) represents a novelty in the field of ecological modeling. The management solutions of our method in terms of land allocation are different from those in Sen and Pal (2013) model. In the case of Iran, many socio-ecological-economic strategies and policies should be necessary for improving the agricultural sector. More specifically, on the basis of rainfall amounts and spatial patterns, this approach can represent a decision-support system able to define strategies for additional water storage useful to support crop production. Furthermore, the availability of water together with the sustainable use of fertilizers can mitigate the risk of land degradation, guaranteeing people employment, food security, and economic profits. Although the present methodology seems to solve the problem of multi-goals decision-making, the inclusion of spatial relationships is able to introduce dependencies between the management of land use in adjacent areas, making the present approach nearer to real-world functioning. [Display omitted] [ABSTRACT FROM AUTHOR]

• The paper presents simulation of air pollution in urban canyons was considered. • Tree and grass barriers were used as a porous medium to protect nearby houses. • Solved by modified Navier-Stokes and species equations to account the porous media. • For modeling, trees and barriers with porosity properties were close to reality. This paper is investigated the comprehensive study of the nitrogen dioxide pollution distribution in a specific street in Almaty city, Kazakhstan. It was given a generalized description of the porous medium and the effect of porosity on the spread of nitrogen dioxide. The calculated obtained results were analyzed by comparison with the previous data. Based on the study of different cases, it was detected that the existing of superposed barrier along the road essentially decreases the level of greenhouse gas concentrations near the pedestrian zone. The optimal variant of the combined barrier with a height of 2.5 m and a tree with a total height of 15 m, where the stem is 3 m, and the crown with a height of 12 m, has the property of permeability(porosity), was chosen. This type of barrier normalizes the ventilation properties in the street canyon and protects the pedestrian zone from poisonous gasses. [ABSTRACT FROM AUTHOR]

• Hidden Markov Models are a useful approach for developing ecological indicators. • They are flexible and allow for non-stationarity. • HMMs found shifts in ocean temperature regimes with increasing mean temperatures. • Regime shifts were reflected in both means and variances for seabird indicators. Ecological indicators are important mechanisms for understanding ecosystem change and implementing Ecosystem Based Fishery Management, but the development of useful indicators must account for ecosystem shifts that result in non-stationary processes over time. This necessitates the adoption of more adaptable statistical modeling approaches. Hidden Markov Models (HMMs) provide a robust framework for distinguishing underlying ecosystem shifts from noisy time-series data. In this paper, we illustrate the power of HMMs to develop model-based ecological indicators of non-stationary systems, focusing on two case studies from the California Current Large Marine Ecosystem. In the first case study, we analyze four temperature time series from 1998 to 2022 that are used as indicators for environmental conditions experienced by juvenile salmon in the northern portion of the system. We apply a three-state HMM incorporating temporal trends to account for non-stationarity in the means over time due to overall ocean warming. Output from this model reveals increasing temperatures for all four metrics in the California Current, with most years being assigned to the warmest estimated state. In our second case study, we analyze nine time series of seabird densities in the northern California Current from 2003 to 2022, to demonstrate how HMMs can be useful to identify sets of indicators that reflect different ecosystem processes, including potential seabird predation pressure on juvenile salmon, and have different variances. We found the strongest support for the existence of two distinct temporal regimes in the seabird data, with an abrupt shift occurring after 2010. While mean densities changed slightly for some species, this regime shift can be best characterized with a shift in variances: sooty shearwaters (Ardenna grisea) and Cassin's auklets (Ptychoramphus aleuticus) represented species with densities becoming more variable, while common murres (Uria aalge) and gulls were estimated to have become less variable after 2010. Common murres, Cassin's auklets, sooty shearwaters, pink-footed shearwaters (Ardenna creatopus) and gulls all represent species that may be useful indicators of change in the northern California Current, because of their differential responses to this regime change. Overall, our analysis provides a first step illustrating the potential applications of HMMs to developing ecosystem indicators in non-stationary systems and a framework that is widely useful for applications to ecosystems around the world. [ABSTRACT FROM AUTHOR]

In this paper, we assess the regrowth capacity of tropical forest plots by developing an original computational procedure based on statistical model checking methods. We calibrate a new mathematical model of forest dynamics with respect to post-logging data, produced in the Amazon basin. Our mathematical model is determined by a mechanistic system of ordinary differential equations and integrates a new nonlinear aging term, which is necessary to reproduce the complex post-logging dynamics of the tropical forest plots. Our method is based on an efficient algorithmic procedure that explores the parameter space of the model and computes a score for each parameter value, depending on the distance between the trajectory of the model and the data. We distinguish a group of four reference plots, for which the logging was the most intense, from another group of five non-reference plots, which are fitted a posteriori. Our results provide a set trajectories of the new model, which successfully fit the non-monotone post-logging data on each forest plot, in spite of the high level of biological variability identified in the study site. Rather than a unique set of parameters, we return a small cell of parameters, extracted from the parameter space, which contains in its close vicinity several relevant sets of parameters that are equally able to reproduce the regrowth dynamics of distinct tropical forest plots. The size of the cells that should be extracted from the parameter space increases with the level of biological variability. Finally, we show how to use the calibrated mathematical model for simulating logging scenarios, so as to better understand the temporal dynamics of forest regrowth. • We assess the regrowth capacity of tropical forest plots. • We calibrate a new mathematical model of forest dynamics with respect to post-logging data, produced in the Amazon basin. • The calibrated forest dynamics model can be used to experiment relevant logging scenarios. • Our approach is based on a statistical verification algorithm that provides sharp guarantees. • Our computational procedure represents a genuine advance on the calibration process of ecological or biological models. [ABSTRACT FROM AUTHOR]

The diamondback moth is one of the world's most destructive pests. Targeting plants such as broccoli and cabbage, they have few natural predators, reproduce effectively, and have been known to establish pesticide resistance over time. In this paper we investigate management of diamondback moth via a bacterial pesticide at a harvesting company. Recognizing evidence of pesticide resistance in the observed data, we quantify this phenomenon mathematically within an age-structured ordinary differential equation model. Significant improvement in the predicted larval population is seen using our model in comparison to an existing model of the same outbreak, in particular in fitting larval growth via incorporation of pesticide resistance. Model predictions of egg and pupal populations are also compared to observed data to gain insight into model accuracy across the life cycle of the diamondback moth. • Mathematical model for response of diamondback moths to biological control. • Age-structured model that incorporates all life stages of the diamond back moth. • Experiment incorporates repeated release of biological control. • Focus on modelling of repeated pesticide release and reduction in efficacy over time. • Establishes a mathematical description of pesticide resistance of diamondback moths. [ABSTRACT FROM AUTHOR]

Intraspecific variability plays a key role in modeling population dynamics and ecological interactions. It is also a fundamental issue for agriculture, livestock production, and aquaculture. Dynamic Energy Budget (DEB) theory is a metabolic theory that can provide insights for these applications, as it can accurately predict the response of an organism to changes in environmental conditions. However, DEB model parameters are typically estimated for the species using mean trait values, neglecting individual differences. With advancing data collection technologies, there is a growing need to leverage individual data to calibrate DEB models. In this paper, we propose a multi-tier methodology for estimating individual-specific DEB parameters based on techniques and principles of the standard DEB estimation procedure. Individual data is often not sufficiently complete, therefore we compliment it available data on organisms of the same species. To avoid estimation bias, we employ a hierarchical tier structure. Data is categorized into tiers, depending on how general it is. In each tier, only a subset of the parameters is estimated depending on available data. The estimation proceeds in a top-down approach, starting with the most general tier and culminating in the individual tier. Values obtained in previously estimated tiers are used as pseudo-data to anchor the parameter estimation. In this way, we obtain biologically sensible individual parameter estimates in an efficient manner. As a case study, we apply our methodology to a dataset that contains weight and feed intake measurements for 52 individuals of the Mertolenga cattle breed from two performance trials. We conclude that variability in the surface-specific maximum assimilation rate and the digestion efficiency is needed to accurately model the individual data. Furthermore, the analysis of tier parameters and individual parameter distributions uncovers differences in the diets fed to the animals. • Methodology to estimate individual parameters for organisms in a DEB framework. • Hierarchical structure addresses information gaps and estimation bias. • Regularization ensures biologically reasonable parameter estimates. • Case study application on weight and feed intake data of Mertolenga cattle. • Parameter analysis uncovers differences in the diets fed to the animals. [ABSTRACT FROM AUTHOR]

Freshwater mussels (Bivalvia: Unionida) are an ecologically important faunal group. Excessive sediments, both in suspended and deposited formats, are believed to have negative effects on survival of freshwater mussels. However, there is a lack of quantitative tools for assessing the impact of abrupt and excessive sedimentation on freshwater mussel habitats. This gap in knowledge poses challenges for construction planning that necessitates evaluating sedimentation effects on mussels. In this paper, we present a simple Lagrangian particle tracking (LPT) model designed to investigate the downstream distances from the sediment release point where mussels may face risks of sediment exposure and burial during episodic sedimentation events. We validated the model in predicting the deposition of sands ranging from 125 to 625 μ m in diameter within an open channel. Subsequently, we applied the LPT model to a representative stretch of mussel habitats in the lower Osage River, Missouri, USA. Implementing the LPT model using computational fluid dynamics analysis of river hydrodynamics, we investigated the downstream distances impacted by a hypothetical sedimentation event. The results indicate that the mussels within tens and hundreds of meters may be at risk of burial under low and high flow conditions, respectively. Mussels within hundreds and potentially thousands of meters may be at risks of sediment exposure under low and high flow conditions, respectively. The impacted distance is highly dependent on the sediment diameters and flow conditions. These results contribute valuable insights into the potential impacts of sedimentation on freshwater mussel habitats, aiding in informed decision-making for construction projects near these critical aquatic ecosystems. • A Lagrangian particle tracking model is developed to study sediment impact on mussels. • The location of sediment-streambed is modeled to inform sediment exposure risk to mussels. • The location of sediment deposition is modeled to inform risk of mussel burials. [ABSTRACT FROM AUTHOR]

• SEM models interactions among climate, forest composition, deer, and recreation. • Climate change will cause a shift from boreal to temperate species. • Forest composition affects recreation in the presence of climate and deer. • Climate, mainly temperature, affects both forest composition and recreation. • Model results could help inform better climate adaptation strategies. Climate change will likely cause changes in the composition of forests worldwide, with consequent impacts on forest ecosystems and the ecosystem services they provide. In northeastern Minnesota, climate change will likely cause a shift from boreal to temperate forest by the end of the century. Such a shift will affect species populations such as deer and outdoor recreation, a valuable ecosystem service with over $4 billion in annual expenditures in hunting, fishing, and wildlife viewing. In this paper, a structural equation modeling (SEM) framework is used to model four key variables, climate change, deer, forest composition, and outdoor recreation, as an interactive system with data from the Laurentian Mixed Forest (LMF) region of Minnesota. A series of SEM models of increasing complexity are used to draw out the individual and system level effects. The results show: a strong empirical relationship between forest composition and recreation; climate affects both forest composition and recreation, mainly through temperature effects; deer affects both forest composition and recreation. A systems modeling approach like SEM is needed because there are important complex interactions, such as the effect of deer on forest composition and of forest composition on deer. [Display omitted] [ABSTRACT FROM AUTHOR]

• The EERO is based on the combined use of dynamic models, probabilistic risk assessments and observational data. • This method allows obtaining the ecological risk intra-annual variations. • This approach allows calculating the corresponding acceptable probability of anthropogenic impact. • This is important for coordination of ecological and economic requirements in safe development of shelf and in costs minimizing. This paper proposes a method for mathematical modeling and ecological risk assessment in marine ecosystems under the combined action of natural, anthropogenic, climatic and invasive factors. The method is based on the combined use of dynamic models, probabilistic eco-screening risk assessments and observational data (EERO). Main advantage of the proposed approach in comparing with the already known methods lies in modeling the stressors effect on the ecosystem main components intra-annual functioning mode. This allows obtaining the ecological risk intra-annual variations, identifying the intra-year intervals of increased danger and calculating the corresponding acceptable probability of anthropogenic impact. Risk simulation examples in regard to the temperate latitude regions (Northern Caspian) highly productive ecosystems and to the Arctic area low productive ecosystems are provided. Results obtained brought to nontrivial conclusions on the ecosystems vulnerability and on the necessity to prioritize ecological risk calculation followed by assessment of the allowable exposure probability. In the environmental and economic aspect, the use of EERO would help to minimize economic expenses in the development of shelf resources. Calculation of the intra-annual ecological risk variations and the corresponding calculation of the anthropogenic impact allowable probability would make it possible to vary expenses on the ecological protection measures. The volume of such measures would be determined by increasing or decreasing requirements to the acceptable anthropogenic load calculated in accordance with the model. The paper substantiates the primacy nature of determining the acceptable ecological risk value and further the acceptable intensity of anthropogenic impact that opens a possibility to receive answers to questions on the basic acceptability of accidents of a certain scale. The method is efficient in assessing the risk to ecosystems with varying degrees of vulnerability. The proposed approach is important for practical coordination of ecological and economic requirements in safe development of shelf resources and in costs minimizing. [ABSTRACT FROM AUTHOR]

Highlights • The gross margin of two types of dairy farms was compared under five different scenarios. • CAN, which is widely used by Irish farmers, is the least profitable and environmentally efficient source of nitrogen. • In the absence of abatement strategies, farmers would have to reduce livestock numbers in order to achieve potential GHG emission constraints. • The application of urea + NBPT allows for more livestock to be kept on the farm while still meeting the GHG emissions constraint. Abstract Ireland's ambitious plan to expand primary agricultural production and its commitment to reduce its non-ETS GHG emissions by 20% compared with 2005 emission levels constitute a considerable challenge for Irish farmers. Nitrous oxide emissions produced as a result of the application of artificial fertiliser accounts for 16% of Ireland's agricultural GHG emissions (Teagasc, 2017a). The use of urea combined with NBPT has the potential to reduce GHG emissions from agriculture when compared with conventional fertilisers. This paper presents a farm level model which maximizes farm gross margin subject to constraints on production factors (labour, land etc.) as well as agronomic constraints (stocking rate, fertilisers, feed, etc.). The aim of this paper is to compare the farm gross margin of two dairy farm types under a base scenario with the results of five other scenarios which consider varying levels of emissions reduction targets and the potential of urea combined with NBPT as an abatement technology. Results inferred that there is a potential for urea combined with NBPT to offset nitrous oxide emissions from fertiliser application and hence contribute to Ireland's GHG reduction target. [ABSTRACT FROM AUTHOR]

Abstract The world food price crisis in 2007/08 has aroused worldwide attention to the global food price volatility and food self-sufficiency issues. This paper modelled the entire environment of food production and transaction from a holistic view by a Food-Energy-Water (FEW) nexus in order to reveal the hidden connections related to the food self-sufficiency issue, including the interdependencies of food production with its restraining factors (hybrid energy, hybrid water), other production sectors, and international exchanges. This paper mapped all direct and indirect flows in the FEW nexus and projected a potential Emission Trading Scheme (ETS) to figure out the impacts of policies on FEW nexus flows, nexus robustness, total input of all sectors, and household expenditures in Japan and China. The results show that the pattern of food-related extraction flows was more imbalanced than the pattern of hybrid energy flows, due to the high dependence of Japan on the food supply of China (16.11% of total food-related extractions). An ETS may increase Japan's total household expenditure on imported goods from China in the fields of sugar refining (1.3096%), processing vegetable oils and fats (0.1164%), processing of meat cattle (0.1010%), as well as slightly decrease the system robustness of the total nexus. [ABSTRACT FROM AUTHOR]

Abstract Rhino populations are at a critical level due to the demand for rhino horn and the subsequent poaching. Wildlife managers attempt to secure rhinos with approaches to devalue the horn, the most common of which is dehorning. Game theory has been used to examine the interaction of poachers and wildlife managers where a manager can either 'dehorn' their rhinos or leave the horn attached and poachers may behave 'selectively' or 'indiscriminately'. The approach described in this paper builds on this previous work and investigates the interactions between the poachers. We build an evolutionary game theoretic model and determine which strategy is preferred by a poacher in various different populations of poachers. The purpose of this work is to discover whether conditions which encourage the poachers to behave selectively exist, that is, they only kill those rhinos with full horns. The analytical results show that full devaluation of all rhinos will likely lead to indiscriminate poaching. In turn it shows that devaluing of rhinos can only be effective when implemented along with a strong disincentive framework. This paper aims to contribute to the necessary research required for informed discussion about the lively debate on legalising rhino horn trade. [ABSTRACT FROM AUTHOR]

Highlights • Studied the dissipation process of the supersaturated TDG in water with vegetation based on first-order kinetic model. • Developed an adsorption model of the vegetation’s effect on the supersaturated TDG. • The adsorption rate in the model is determined by the material properties. • The model is suitable for static conditions. Abstract Flood plays a vital role in aquatic organisms’ subsistence and reproduction. It can bring spawning signals to fishes and excessive foods fattening fishes up in the flood plain. Nowadays, with an increasing number of high dams being put into operation, huge changes have happened in the process of natural flood. In order to maintain the ecological function of flood, sometimes hydropower stations have to make artificial flood peak by the power station’s spillway discharging. Water discharge from high dams leads to the supersaturation of total dissolved gas (TDG), thus causing fish to suffer from gas bubble disease (GBD) and even death. The downstream water level rises during the period of discharge, inundating part of the vegetation on the bottomland. The existence of vegetation can affect the hydrodynamic characteristics of flood water flows and then change the transportation and dissipation of the supersaturated TDG. The stems and leaves of the submerged vegetation provide a large amount of solid-liquid interfaces, which adsorb gas dissolved in water, consequently influencing the dissipation process of supersaturated TDG. In this paper, research about the effect of vegetation on supersaturated TDG is conducted underpinned by laboratory mechanism experiments. The supersaturated TDG dissipation process with vegetation existing in the water was obtained. The results indicate that planting vegetation in water can effectively bolster the releasing of TDG, and as the density of vegetation increases, the promotion effect is commensurately enhanced. Based on the experimental data, this paper proposes a supersaturated TDG dissipation model describing the function of the vegetation’s wall adsorption of the TDG, and the formula for the adsorption flux F a of supersaturated TDG in a unit area over a unit time is achieved. This paper obtains a significant parameter representing how fast the vegetation’s surfaces adsorb the supersaturated TDG, namely, the adsorption rate k a. It is determined that k a is solely related to the vegetation’s material and surface characteristics. [ABSTRACT FROM AUTHOR]

Input capacity constraints means a firm cannot make its output decision based on the first - order optimal condition of profit maximization because it does not have enough inputs. Input capacity constraints have a significant effect on production quality and firm competition, especially on food safety management. This paper tries to provide a new perspective for quality operation in supply chain management. It also designs a new mechanism for quality regulation. After distinguishing between quantity-sensitive input capacity constraints and quality-sensitive input capacity constraints, this paper demonstrates a theoretical analysis about the impact of input capacity constraints on food quality and quality regulation by game theory. The results show that both kinds of capacity input constraints lead to a lower level of output, quality and consumer surplus, but a higher level of prices. In this study, we design a quality regulation to stimulate the growth of high quality food products. More importantly, this paper proves that reward is more effective in food quality guarantee programs than punishment or fine based programs. The conclusions are helpful for addressing food safety problems for both the supervisors and firms. [ABSTRACT FROM AUTHOR]

• Build a multi-objective method for improving LEN functional and structural connectivity. • Identify key locations for improving structural connectivity based on graph theory. • Introduce "integral index of probable connection" into network link additional technique. • Search corridor paths with maximum functional connectivity using greedy algorithm. • Use hierarchical rules with different priorities to set the greedy algorithm. By building connectivity between ecological spaces, the landscape ecology network (LEN) can effectively prevent further habitat fragmentation and loss in urbanizing areas. The existing LEN construction method fails to consider the multiple optimization goals of improving network structural and functional connectivity. This paper aimed to provide a novel method to improve connectivity for existing LENs in urbanizing areas. We took an urbanizing area in China as an example and extract existing LEN based on ecological priorities. On this basis, from the dimension of structural connectivity optimization, we used the network link additional technique based on "integral index of probable connection" to identify 50 optimal locations of additional corridors that could significantly improve the network structural connectivity. From the dimension of functional connectivity enhancement, we applied the greedy algorithm based on hierarchical rules with different priorities to search for corridor paths with maximum ecological function and low ecological and construction resistance. The optimized LEN contained 43 ecological sources and 74 ecological corridors. The dual dimension method proposed in this paper can improve the functional and structural connectivity of existing LEN, and greatly save the optimization time and efficiency of the network, to provide targeted ecological space protection schemes for urbanizing areas. [ABSTRACT FROM AUTHOR]

This work compiles the conceptual and practical proposals of various research groups regarding interesting properties of mathematical models. Although different types of mathematical models are mentioned, phenomenological-based semi-physical models (PBSM) are the main focus of this paper. The partitioning of the process to be modeled, the scalability of the model, and the interpretability of its parameters are presented as important properties of the model that help to understand and use the mathematical model. The use of these properties is illustrated with examples, providing the respective bibliographical reference where an expanded presentation of each example can be reviewed. The importance of each model property presented is explained from the inherent condition of the model as a mathematical object and from the expectation of the final users of the model. The confidence in the model is increased when the user knows how the model is obtained. These properties provide valuable information to the end use of the model. Therefore, the modeler must understand and apply this knowledge during model deduction and write, in the model report, how the mentioned properties were tested. • Partitioning in PBSM maintains process complexity and enhances model precision. • Scalability in PBSM ensures accurate predictions across different system scales. • PBSM interpretable parameters foster model transparency and informed decision-making. • Comprehensive and transparent reports of PBSMs, fostering confidence in validation. [ABSTRACT FROM AUTHOR]

Rapid urbanization greatly accelerated economic and social development in key river basins of China, but also created numerous environmental problems, from the local to the global scale, including increased air and water pollution and decreased water supply, local climate alteration and increased energy demands, as well as a major reduction in natural vegetation production and carbon storage/sequestration. The correlation analysis between urban economy and pollution transfer among cities in river basins has become one of highlights in current researches. Based on the Netlogo software platform, this paper establishes a correlation model among cities in the same river basin from the point of view of multi-agent simulation. Moreover, it assesses the relationship between urban economic development and pollution transfer from three aspects: network concentration; network size; link intensity. The influence of network concentration and link intensity on the total economic size and pollution transfer of the entire river basin is similar. In parallel, the influence of network size on the two modelled variables shows an opposite trend, demonstrating that network size has a greater impact on the city. By comparing the trend of economic size and pollution transfer with the change of network concentration in the context of different economic transfer thresholds, results demonstrate that, with the increase of network concentration, the influence becomes more pronounced. Appropriate error analysis was used to validate the simulation results. In particular, according to it, the error between 10-time and 20-time steps simulation is less than 10%. Consequently, the simulation results are valid. This paper, deepening the research on the correlation among river basin cities, lays the foundation for further research on the directional transmission of the economy and pollution. [ABSTRACT FROM AUTHOR]

What is sustainability? Sustainability is a concept that can be defined in many ways depending upon a society's perception of current material needs and the actual material needs of future generations. Much of our ability to achieve sustainability entails developing indicators and measurements that will guide us to this goal. This paper suggests that we can strengthen the prediction of sustainability indicators by adopting a “multiple equilibria” approach for a more effective decision-making process in various sectors of the economy, in ecosystem protection, or in political arenas. There is an emerging need for further development of predictive mathematical models of system sustainability over economic growth models for sustainable resource measurement and management. The objective of this paper is to use computer modeling and differential equations to simulate the “multiple equilibria” of a 3 variable real world system. In our study, we tested the theoretical validity of “multiple equilibria” sustainability modeling through simulated measurements of precipitation and nitrogen runoff into a hypothetical lake. As a quantitative tool to model, the “multiple equilibria” techniques can have tremendous predictive power for business leaders, political decision makers, and environmental scientists, and assist in better management of ecological, economic, and material resources in short-term and long-term end-use scenarios. [ABSTRACT FROM AUTHOR]

Architects working with city planners and developers in the shaping of urban environments typically consider multiple factors in isolation, from urban design and socio-economic relationships to data analyses. Analyses regarding urban life cycle scenarios are exemplar of this trend, with considerations made in isolation at the later stages of the design-development process when the scope for decisions which could ultimately affect the sustainability of an urban environment is much more limited. This paper defines our effort to introduce a new tool, named “Clark’s Crow”, which aims to address this shortcoming by promoting awareness of the impact of different design options through a biophysically based ecological accounting method in the early stages of urban design-development. The tool is used within existing architectural design environments with an aim to offer a socio-ecological analysis during the design decision-making process. Clark’s Crow is underpinned by the emergy analysis method, which aims to consider both the energy, material, and information flows of a system, such as an urban ecology, and to understand both the work of the techno-sphere in constructing our urban environments and that of the geo-biosphere in sustaining such development. Clark’s Crow facilitates emergy analysis in the early stages of urban design, thereby allowing queries regarding material and energy flows to be addressed in conjunction with design choices at this initial stage. In this paper, we demonstrate the effectiveness and features of Clark’s Crow through a case study of development using next generation systems in Manhattan, New York, depicting how an emergy analysis approach can lead to an understanding of the value and impact of speculative buildings towards sustainable design-development. [ABSTRACT FROM AUTHOR]

• Municipal solid waste (MSW) management drives fundamental future shifts. • An innovative and integrated biorefinery (2IB) is evaluated. • Detailed technical information on the 2IB is presented. • An emergy return of about 20 sej is made available to society by investing in 2IB. • 2IB emerges to be a powerful technological alternative to manage MSW. The rampant municipal solid waste (MSW) generation caused mainly by people's lifestyle has required technological solutions to deal with this global issue. This paper assess an innovative and integrated biorefinery (2IB) to manage MSW. The 2IB is rooted in the circular economy concept, and it differs from any other currently existing technological route in managing MSW due to its integrated processes that receives both inorganic and organic fractions at the same time, besides unveiling an innovative aspect related to thermoplastic production. Due to its scientific-based advantages, the emergy accounting (withy 'm') is considered as method to quantify the 2IB environmental performance, comparing the results with alternatives MSW management facilities. Results show that 2IB has similar performance for the three traditional emergy indicators (m-EYR of 1.17, m-ELR of 5.73 and m-ESI of 0.20) compared to incineration and landfilling, indicating low yield, moderate load on the environment, and unsustainable. On the other hand, the emergy return indicator (ERI of 20.33) indicates higher performance for the 2IB, in which for each solar emjoule (sej) – the measure unit of emergy – invested it generates a return of about 20 sej that becomes available as benefits for societal development. From an emergy perspective, results support that 2IB should be considered as a powerful technological alternative to manage MSW. This study contributes from a theoretical perspective with discussions on the emergy accounting procedures applied on systems located at far right side of energy hierarchy, while contributing from a practical perspective by providing technical information about the 2IB that supports decision makers towards more sustainable MSW management systems. [ABSTRACT FROM AUTHOR]

• We compare the performance of predictive models for wildcat hybrid occurrence based on (1) global pooled data across individuals, (2) geographically weighted aggregation of individual models, (3) ecologically weighted aggregation of individual models, and (4) combinations of global, geographical and ecological weighting. • We developed predictive models both using GLM and random forest machine learning to compare the performance of these differing algorithms and how they compare in stationary and nonstationary analyses. • We validated the predicted models in four different ways. • Our results show that validation using hold-out data from the individuals used to train the model provides highly biased assessment of true model performance in other locations, with random forest in particular appearing to perform exceptionally (and inaccurately) well when validated by data from the same individuals used to train the models. • Each of these three independent validation data sets gave a different result in terms of the best overall model. • The best overall model produced for potential wildcat occurrence and habitat suitability was obtained by an ensemble average of the global generalized linear model (GLM) and random forest models with the ecologically weighted GLM and random forest models. Species distribution modeling has emerged as a foundational method to predict occurrence and suitability of species in relation to environmental variables to advance ecological understanding and guide conservation planning. Recent research, however, has shown that species-environmental relationships and habitat model predictions are often nonstationary in space, time and ecological context. This calls into question modeling approaches that assume a global, stationary ecological realized niche and use predictive modeling to describe it. This paper explores this issue by comparing the performance of predictive models for wildcat hybrid occurrence based on (1) global pooled data across individuals, (2) geographically weighted aggregation of individual models, (3) ecologically weighted aggregation of individual models, and (4) combinations of global, geographical and ecological weighting. Our study system included GPS telemetry data from 14 individual wildcat hybrids across Scotland. We developed predictive models both using Generalized Linear Models (GLM) and Random Forest machine learning to compare the performance of these differing algorithms and how they compare in stationary and nonstationary analyses. We validated the predicted models in four different ways. First, we used independent hold-out data from the 14 collared wildcat hybrids. Second, we used data from 8 additional GPS collared wildcat hybrids from a previous study that were not included in the training sample. Third, we used sightings data sent in by the public and researchers and validated by expert opinion. Fourth, we used data collected by camera trap surveys between 2012 – 2021 from various sources to produce a combined camera trap dataset showing where wildcats and wildcat hybrids had been detected. Our results show that validation using hold-out data from the individuals used to train the model provides highly biased assessment of true model performance in other locations, with Random Forest in particular appearing to perform exceptionally (and inaccurately) well when validated by data from the same individuals used to train the models. Very different results were obtained when the models were validated using independent data from the three other sources. Each of these three independent validation data sets gave a different result in terms of the best overall model. The average of independent validation across these three validation datasets suggested that the best overall model produced for potential wildcat occurrence and habitat suitability was obtained by an ensemble average of the global Generalized Linear Model (GLM) and Random Forest models with the ecologically weighted GLM and Random Forest models. This suggests that the debate over whether which of GLM vs machine learning approaches is superior or whether global vs aggregated nonstationary modeling is superior may be a false choice. The results presented here show that the best prediction applies a combination of all of these approaches in an ensemble modeling framework. [ABSTRACT FROM AUTHOR]

• A novel R package for modelling ecological dynamics was developed. • A three-cycle procedure was proposed to analyse a model and decide its complexity. • Graphical, analytical, and numerical tools were integrated to view model dynamics. Population dynamics modelling plays a crucial role in understanding ecological populations and making informed decisions for environmental management. However, existing software packages for dynamical system modelling often lack comprehensive integration of techniques and guidelines, limiting their practical usability. This paper introduces ecode, a novel package for modelling ecological populations and communities using ordinary differential equation systems, designed with a user-friendly framework. By following a three-cycle procedure, users can easily construct ecological models and explore their behaviours through a wide range of graphical, analytical, and numerical techniques. The package incorporates advanced techniques such as grid search methods and simulated annealing algorithms, enabling users to iteratively refine their models and achieve accurate predictions. Notably, ecode minimises external dependencies, ensuring robustness and reducing the risk of package failure caused by updates in dependencies. Overall, ecode serves as a valuable tool for ecological modelling, facilitating the exploration of complex ecological systems and the generation of informed predictions and management recommendations. [ABSTRACT FROM AUTHOR]

Classical kriging models use Euclidean distance when modeling spatial autocorrelation. However for regions with irregular boundaries and holes, such as estuaries and coastlines, a measure of within-domain distance may capture a system's proximity dependencies more accurately. Standard kriging techniques are not guaranteed to yield a valid covariance structure when defined in terms of non-Euclidean distances. In this paper, we develop a new kriging model for irregularly shaped domains. Our model uses an approximation to a diffusion process to define a valid covariance structure that reflects the domain topology. A covariance matrix is defined through the use of random walks on a lattice, process convolutions, and the kriging equations. A simulation study demonstrates that for commonly encountered topologies, our diffusion kriging estimator is superior to a kriging estimator based on shortest within-domain distance. We also illustrate our method using water quality data from Puget Sound and Lake Peipsi to map chlorophyll concentration. [ABSTRACT FROM AUTHOR]

In this paper, we propose a class of resource-based growth models with delays in algal digestion and detritus-nutrient recycling, and investigate the model based on two different survival scenarios of algae during nutrient transformation. One scenario considers the survival rates of algae during nutrient uptake, while the other overlooks this factor. We find a significant difference in the estimated time window required for clearing the lake of detritus between the two models, and both are longer compared to the model without algal digestion delay. Moreover, the internal equilibria of both scenarios undergo an infinite number of stability switches, however, the key parameters leading to these stability switches differ. Notably, in the model accounting for algal death during nutrient uptake, there exists a safe zone where the stability of the internal equilibrium remains unaffected by the detritus-nutrient recycling delay, provided that the algal digestion delay falls within a suitable range. The findings derived from this study can provide valuable insights for the development of efficacious approaches in safeguarding the ecological integrity, managing algal blooms, facilitating sustainable fishery practices, and fostering favorable economic outcomes within the realm of water resources advancement. • Models with algal digestion and detritus-nutrient recycling delays. • Detritus clearance time variations under different algae survival scenarios. • Infinite stability switches in internal equilibria. • Safety zone identified in the model with algal death during nutrient uptake. • Insights for algal bloom management, fisheries, and water resource enhancement. [ABSTRACT FROM AUTHOR]

• We explore spatially varying limiting factors in species-environment relationships using high resolution telemetry data from 14 individual wildcat hybrids distributed across geographical and environmental gradients in Scotland. • We proposed five hypotheses for how nonstationary limiting factors related to the strength of predictor variable effect size. • Our results show that assumptions of global, stationary habitat associations are likely not met in many habitat models. • We found that there were strong relationships between nonstationarity and spatially varying limiting factors. • Future habitat modeling research should explicitly consider scale and context dependence in a nonstationary modeling paradigm. Species distribution modeling is widely used to quantify and predict species-environment relationships. Most past applications and methods in species distribution modeling assume context independent and stationary relationships between patterns of species occurrence and environmental variables. There has been relatively little research investigating context dependence and nonstationarity in species distribution modeling. In this paper we explore spatially varying limiting factors in species-environment relationships using high resolution telemetry data from 14 individual wildcat hybrids distributed across geographical and environmental gradients in Scotland. (1) We proposed that nonstationary limiting factors would be indicated by significant association between statistical measures of variability of predictors and the predictive importance of those variables. (2) We further proposed that most of the limiting factor relationships observed would be related to spatial variation and a lesser amount to mean value of environmental variables within individual study sites. (3) Additionally, we anticipated that when there was a relationship between variation of an environmental factor and its importance as a predictor this relationship would be positive, such that higher variation would be associated with higher importance of the variable as a predictor (following the theory of limiting factors). (4) Conversely, we proposed that when there was a relationship between the mean value of an environmental variable and its importance as a predictor this relationship would be roughly evenly split between positive and negative relationships, given that environmental variables could become limiting either when they are highly abundant or high value, or when they are rare or low value in a particular landscape, depending on the nature of the species-environment relationship for that ecological variable. (5) Finally, we hypothesized that the frequency of supported limiting factor relationships would differ among variable groups, with variables that were directly related to key environmental resources more likely to be limiting than those that would have more indirect impacts on wildcat hybrid habitat selection or foraging. Our results show that assumptions of global, stationary habitat associations are likely not met in many habitat models, requiring explicit consideration of scale and context dependence in a nonstationary modeling paradigm. We found that both the mean value and the standard deviation are strong predictors of whether that variable will be limiting and differentially important as a predictor of occurrence. We confirmed that limiting factors become more limiting when it has higher variability across the sampled data, or when it is rare or not abundant. The frequency of supported limiting factor relationships differed among variable groups, with variables that were directly related to environmental resources likely to be essential for wildcat hybrid ecology more likely to be limiting than those that would have more indirect impacts on wildcat hybrid habitat selection or foraging. [ABSTRACT FROM AUTHOR]

· Most marine turtles have a typical annual nesting season, and the number of nests laid during a season can be used as a proxy for population size to define trends and conservation status. · However, in some cases, two peaks in female emergences are detected during single year. · We show that estimates that fail to consider this bimodality into account can provide biased results both in terms of describing the phenology and quantifying the number of nests. · The shifts in nesting could be a strategy to adapt to present-day incubation temperatures, hatching success rates, and sex ratios in the context of climate warming. The seasonality of egg-laying in marine turtles is a well-known phenomenon that has been used to determine population trends based on an integration of the number of female emergences or nests during a season. However, in some cases, several peaks in female emergences are detected during single year. In this paper, we explore three such situations, two involving leatherback turtles laying eggs in French Guiana and in Northwest Coast of Papua, Indonesia and another involving green turtles in the Indian Ocean. In the three cases, we demonstrate the existence of a bimodal nesting season using a new statistical model. With this model, we show that estimates that fail to consider this bimodality into account can provide biased results both in terms of describing the phenology and quantifying the number of nests. The origin of these different groups of females is discussed based on the available information. This new model opens the door to a more rigorous analysis of egg-laying seasonality in marine turtles. Such rigor is essential in the current context of climate change, where shifts in seasonality can have a major influence on these species due to their temperature-sensitive sex determination. [ABSTRACT FROM AUTHOR]

Theoretical developments are needed to interpret the increasing amount of large-scale spatial data about past settlements. So far, settlement patterns have mostly been considered as passive imprints of past human activities and most theories are limited to ecological processes. Locational and spatial interactions have scarcely been included as long-term driving forces of settlement systems but hold promise to explain large-scale patterns. This paper proposes a conceptual model for long-term spatial adaptive settlement systems based on the complex adaptive systems framework and both spatial and cross-scale interactions. The goal of the model is to find new ways of interpreting archaeological location data and understand settlement systems as emerging from micro-choices of population units interacting in space. The conceptualisation is carried out on a level that it can be used to bridge hunter-gatherer and urban theories. We first describe settlement patterns based on concepts from archaeological locational studies and social-ecological systems. Second, we identify the abstract spatial and aspatial entities of the system and describe the potential relations between them. Using knowledge from previous research, we then map both empirically observable and abstract system entities and predict links between them in order to come up with an overarching conceptual framework. The system is based on residential choice mechanisms and exposes several cross-scale feedback loops between the micro-level choices and the settlement system emerging at the meso-level. We finally argue that the proposed adaptive settlement system framework has the potential to bring new insights into long-term processes, especially through dynamic spatial simulation, and at the same time, provides an interpretational framework for archaeological records and empirical spatial analysis. Examples of its applications in archaeological research are introduced. • A framework linking micro-level choices to the emergence of settlement systems. • Integrates social-ecological systems and spatial interaction for locational data analysis. • Identification of feedback loops between individual choices and settlement dynamics. • Archaeological locational models help to decode past social-ecological systems. [ABSTRACT FROM AUTHOR]

Feeding being linked to surface area and maintenance to volume causes juvenile-driven cycles in individual-based population models (IBM's). This combination of traits induces self-synchronisation of individuals: at some low food level, small individuals can still grow, but large ones cannot. Since Dynamic Energy Budget (DEB) models have these features, which are well-tested for individuals in the Add_my_Pet collection, DEB-based population models have such juvenile-driven cycles in simple homogeneous reactors. These cycles are, however, not seen in practice. This paper explores ways to reduce or avoid such cycles in a realistic way, keeping the model as simple as possible, and comes with recommendations. Some of the fixes also repair related artefacts of too simple population models, such as competitive exclusion, the paradox of enrichment and merry-go-around. A size-dependent hazard, which is essential for species with many small offspring, and details on nutrition are unavoidable in realistic models for physiologically structured population dynamics. [Display omitted] • Population cycles originate from feeding/maintenance being surface area/volume linked. • 7 realistic mechanisms are evaluated to reduce such unrealistic consequences. • Thinning, scatter in parameters and pulsed input reduces the cycles and avoids them. • Social interaction reduces the cycles and avoids competitive exclusion. [ABSTRACT FROM AUTHOR]

• This study reviewed four process models including DNDC, APSIM, DayCent, and STICS. • DNDC and DayCent perform better in simulating CH 4 emissions from rice. • APSIM is effective for simulating N 2 O emissions from maize and wheat. • Model mechanisms (44.57%) are the most influential factors affecting the accuracy of simulation. Agricultural ecosystems have long been recognized as significant greenhouse gas (GHG) sources. To accurately quantify GHG emissions, researchers have developed various process models. However, there are no summary studies of comparative model simulations of GHG emissions from different crop systems. This study compared four widely used process models: APSIM, DNDC, DayCent, and STICS, analyzing their mechanisms, input variables, and simulation results from different crops in simulating GHG emissions. In this study, a total of 94 relevant peer-review literature papers were considered. The research found that these models have strengths in simulating GHG emissions from different crops, but also have certain limitations. DNDC and DayCent performed better in simulating methane (CH 4) emissions from rice, while APSIM was more effective in simulating nitrous oxide (N 2 O) emissions from maize and wheat. The main factors affecting the simulation results include model mechanisms, management practices, climate, and data availability. To improve model accuracy, it is recommended that future research expands model applicability, evaluates models using standardized measured data, and enhances adaptability by optimizing algorithms and incorporating simulations of key processes. [ABSTRACT FROM AUTHOR]

This paper introduces dynamic configurational changes for patchy mosaics, parameterizable dilation and/or erosion processes of units in a vectorial landscape. Patchy-based models are rare although this conceptual framework could yield insights to the functioning and dynamics of agricultural and natural landscapes. Compared to common raster-based models, they are more parsimonious and intuitive, but their algorithmic computations are challenging. The aim of this study is to implement polygon dilation throughout Minkowski sum on polygons, along with their associated formal grammar, in the DYPAL modeling platform. This computation is a challenging open-problem and this paper provides a first working approach along with the methodology for a generic implementation framework. As an example, this paper illustrates configurational changes on a complex forest-savannah dynamics in a tropical biodiversity hotspot, New Caledonia. Finally, perspectives for configurational changes in landscape modeling are discussed. [ABSTRACT FROM AUTHOR]

Food systems will need to undergo considerable transformation. To be better prepared for and resilient to uncertainty and disturbances in the future, resource users and managers need to further develop knowledge about the food and farming system, with its dominating feedback structures and complexities, and to test robust and integrated system-based solutions. This paper investigates how participatory system dynamics modeling can be adapted to groups at the community level with low or no formal educational background. The paper also analyses the refinement of workshop participants’ mental models as a consequence of a participatory system dynamics intervention. For this purpose, we ran two workshops with small-scale farmers in Zambia. Analysis of workshop data and post-workshop interviews shows that participatory system dynamics is well adaptable to support an audience-specific learning-by-doing approach. The use of pictures, objects and water glasses in combination with the basic aspects of causal loop diagramming makes for a well-balanced toolbox. Participants acquire understanding that is also relevant beyond systems thinking in that it offers a range of practical insights such as a critical evaluation of common food security strategies. [ABSTRACT FROM AUTHOR]