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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]

• Good modelling practice requires appropriate spatial resolution environmental data. • Unpredictability of the MAUP bias in ecological modelling. • Use of lower resolution data leads to an oversimplification of modelled extent. • Inappropriate spatial resolution can lead to ineffective management decisions. • Challenges of real-world decision making where data is not at a suitable resolution. Effective management of marine ecosystems requires informed decision-making based on accurate, comprehensive and appropriate data. Mapping habitats can form an important part of these processes so that decision makers can ensure the pressures exerted by human activities can be assessed while considering the sensitivity of the environment. Predictive models can be utilised to understand the distribution of species and habitats (e.g. biotopes) without incurring the expense of wide scale surveys. If predictive models are used in decision-making care must be taken in interpreting model results. The spatial resolution at which habitat modelling is conducted can greatly influence the decision outcomes. Whilst national resolution habitat maps serve as valuable resources for informing overarching policy making, for development level decisions fine resolution habitat information is needed. This paper explores the importance of spatial resolution modelling in marine management decision-making processes, using four spatial resolutions (50 m, 100 m, 200 m and 500 m) to model the presence of a protected habitat, maerl beds, within the Fetlar-Haroldswick Marine Protected Area in the Shetland Islands, Scotland. Outputs were compared for model performance between the resolutions and area of modelled maerl bed coverage. Simulations of real-world marine activities, explore the magnitude of overlap attributable to varied spatial resolution models, with an emphasis on the presumed 'need' for management. This study shows the importance of considering spatial resolution in modelling outputs and highlights the challenges associated with using models to guide decision-making, direct pressures on protected habitats, and cumulative impacts. It carries significance for maximising economic opportunity while safeguarding marine features. For real-world applications coarse resolution data may suffice for strategic, large-scale decisions, but finer resolutions are imperative for consenting or managing individual marine activities. By emphasising the need for appropriate spatial resolution modelling, these findings contribute to the development of sustainable management strategies that are appropriate to the scale of the decision. Addressing the complexities of real-world decision-making and understanding the magnitude of spatial resolution required for the marine environment are a crucial principle that can also enhance and be applied to other disciplines including, terrestrial ecology, urban planning and the assessment of potential climate change impacts. Failing to model appropriately means that real-world pressures and impacts occurring on a finer scale then the available data may have their impacts over or underestimated, hindering effective governance. Whilst we are striving to meet our national and international obligations and objectives through effective marine governance, this paper highlights the challenges of real-world decision-making where data is not yet available at a suitable resolution to address these issues. [ABSTRACT FROM AUTHOR]

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]

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]

• 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]

• We identify critical liana traits that contribute to establishing a liana plant functional type in vegetation models for tropical rainforests. • We review key processes to properly represent lianas in ecosystem models. • We discuss a variety of possible liana implementation strategies with their associated strengths, limitations, computational costs and data requirements, based on our experience with implementing lianas in a cohort (ED2) and individual (FORMIND) based model. Lianas (woody climbers) are crucial components of tropical forests and they have been increasingly recognized to have profound effects on tropical forest carbon dynamics. Despite their importance, lianas' representation in vegetation models remains limited, partly due to the complexity of liana-tree dynamics and the diversity in liana life history strategies. This paper provides a comprehensive review of advances and challenges for mechanistically representing lianas in forest ecosystem models and a proposed path towards effectively representing lianas in these models. Defining a liana plant functional type is a significant challenge because of the high morphological and physiological diversity amongst liana species, and because of their structural association with trees. Here, we identify critical liana traits that likely should contribute to establishing a liana plant functional type, along with key processes to properly represent lianas in ecosystem models. Subsequently, we discuss a variety of possible liana implementation strategies with their associated strengths, limitations, computational costs and data requirements. A fundamental redesign of the tree-centric demographic vegetation models seems appropriate to accommodate the unique growth and competition strategies of lianas. We illustrate the potential of such models with a single-site case study where we disentangle putative mechanisms of liana increasing abundance. Furthermore, we underscore the critical need for comprehensive liana demographic and functional data (including long-term, physiological, and pantropical observations) for the qualitative implementation and evaluation in the proposed modeling efforts. Currently, there is a scarcity of liana data and the data that do exist have a neotropical bias. We finally introduce a new liana functional trait database that can centralize existing liana trait data, incentivize improved data gathering and thus facilitate model development and scientific analyses. [ABSTRACT FROM AUTHOR]

Frequently in socio-environmental sciences, models are used as tools to represent, understand, project and predict the behaviour of these complex systems. Along the modelling chain, Good Modelling Practices have been evolving that ensure — amongst others — that models are transparent and their results replicable. Whenever such models are represented in software, Good Modelling meet Good Software Practices, such as a tractable development workflow, good code, collaborative development and governance, continuous integration and deployment; and they meet Good Scientific Practices, such as attribution of copyrights and acknowledgement of intellectual property, publication of a software paper and archiving. Too often in existing socio-environmental model software, these practices have been regarded as an add-on to be considered at a later stage only; modellers have shied away from publishing their model as open source out of fear that having to add good practices is too demanding. We here argue for making a habit of following a list of simple and not so simple practices early on in the implementation of the model life cycle. We contextualise cherry-picked and hands-on practices for supporting Good Modelling Practice, and we demonstrate their application in the example context of the Viable North Sea fisheries socio-ecological systems model. • You must publish your model software, it is good enough. • Good Practices are central in modelling, software, and research. • Minimum good practices are version control, documentation, a license, and archiving; • add also readable code, a collaboration platform, and distributed redundancy. • Build up your good practices toolkit one by one. [ABSTRACT FROM AUTHOR]

• Observations of speed and direction improve short-term bird migration forecasts. • Wind forecasts further improve the range of individual bird migration forecasts. • Altitude changes may be worthwhile to add in the models as indirect driver of speed. • Mixed flight modes may cause slower flight speeds than forecasted by our model. • Forecast errors in direction are more problematic than speed for most applications. In this paper we present three different models to forecast bird migration. They are species-specific individual-based models that operate on a high spatiotemporal resolution (kilometres, 15 min-hours), as an addition to radar-based migration forecast models that currently exist. The models vary in complexity, and use GPS-tracked location, flying direction and speed, and/or wind data to forecast migration speed and direction. Our aim is to quantitatively evaluate the forecasting performance and assess which metrics improve forecasts at different ranges. We test the models through cross-validation using GPS tracks of common cranes during spring and autumn migration. Our results show that recordings of flight speed and direction improve the accuracy of forecasts on the short range (<2 h). Adding wind data at flight altitude results in consistent improvements of the forecasts across the entire range, particularly in the predicted speed. Direction forecasts are less affected by adding wind data because cranes mostly compensate for wind drift during migration. Migration in spring is more difficult to forecast than in autumn, resulting in larger errors in flight speed and direction during spring. We further find that a combination of flight behaviours – thermal soaring, gliding, and flapping – complicates the forecasts by inducing variance in flight speed and direction. Fitting those behaviours into flight optimisation models proves to be challenging, and even results in significant biases in speed forecasts in spring. We conclude that flight speed is the most difficult parameter to forecast, whereas flight direction is the most critical for practical applications of these models. Such applications could e.g., be prevention of bird strikes in aviation or with wind turbines, and public engagement with bird migration. [ABSTRACT FROM AUTHOR]