Biodiversity-production feedback effects lead to intensification traps in agricultural landscapes

Intensive agriculture with high reliance on pesticides and fertilizers constitutes a major strategy for 'feeding the world'. However, such conventional intensification is linked to diminishing returns and can result in 'intensification traps'-production declines triggered by the negative feedback of biodiversity loss at high input levels. Here we developed a novel framework that accounts for biodiversity feedback on crop yields to evaluate the risk and magnitude of intensification traps. Simulations grounded in systematic literature reviews showed that intensification traps emerge in most landscape types, but to a lesser extent in major cereal production systems. Furthermore, small reductions in maximal production (5-10%) could be frequently transmitted into substantial biodiversity gains, resulting in small-loss large-gain trade-offs prevailing across landscape types. However, sensitivity analyses revealed a strong context dependence of trap emergence, inducing substantial uncertainty in the identification of optimal management at the field scale. Hence, we recommend the development of case-specific safety margins for intensification preventing double losses in biodiversity and food security associated with intensification traps.

An open-access global database of meta-analyses investigating yield and biodiversity responses to different management practices

We here present a database of evidence on the impact of agricultural management practices on biodiversity and yield. This database is the result of a systematic literature review, that aimed to identify meta-analyses that use as their response variables any measure of biodiversity and yield. After screening more than 1,086 titles and abstracts, we identified 33 relevant meta-analyses, from which we extracted the overall estimates, the subgroup estimates as well as all information related to them (effect size metric, taxonomic group, crop type etc.). We also extracted information relative to the empirical studies used for each meta-analysis and recorded the countries in which they took place and assessed the quality of each meta-analysis. Our dataset is publicly accessible and can be used for conducting second-order meta-analyses on the effect of management measures on species richness, taxon abundance, biomass and yields. It can also be used to create evidence maps on agriculture-related questions.

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

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

Research priorities for global food security under extreme events

Extreme events, such as those caused by climate change, economic or geopolitical shocks, and pest or disease epidemics, threaten global food security. The complexity of causation, as well as the myriad ways that an event, or a sequence of events, creates cascading and systemic impacts, poses significant challenges to food systems research and policy alike. To identify priority food security risks and research opportunities, we asked experts from a range of fields and geographies to describe key threats to global food security over the next two decades and to suggest key research questions and gaps on this topic. Here, we present a prioritization of threats to global food security from extreme events, as well as emerging research questions that highlight the conceptual and practical challenges that exist in designing, adopting, and governing resilient food systems. We hope that these findings help in directing research funding and resources toward food system transformations needed to help society tackle major food system risks and food insecurity under extreme events.

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We asked experts for top threats to global food security from extreme events

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We find unresolved governance challenges underpin many of the key threats

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We also asked experts for top outstanding research priorities on this topic

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Coordination to design, adopt, and govern resilient food systems is needed

Heat waves, floods, droughts, pest outbreaks and diseases, financial crises, and human conflicts are threatening the production and supply of food around the world. These extreme events are on the rise, and our ability to prepare for them seems limited. Multiple events occurring at the same time compound the problem. Research teams and policymakers are developing solutions to improve the resilience of food systems, but this is often done in isolation—tackling one problem at a time. In this article we bring together food system experts to identify the top threats over the next two decades and priority research questions to address them. We find that unresolved governance challenges in international relations underpin many of the key threats and that coordinated research is needed to help design and adopt systems of governance for food systems that are resilient to extreme events in the future.

Landscape heterogeneity filters functional traits of rice arthropods in tropical agroecosystems

Biological control services of agroecosystems depend on the functional diversity of species traits. However, the relationship between arthropod traits and landscape heterogeneity is still poorly understood, especially in tropical rice agroecosystems, which harbor a high diversity of often specialized species. We investigated how landscape heterogeneity, measured by three metrics of landscape composition and configuration, influenced body size, functional group composition, dispersal ability, and vertical distribution of rice arthropods in the Philippines. We found that landscape composition and configuration acted to filter arthropod traits in tropical rice agroecosystems. Landscape diversity and rice habitat fragmentation were the two main gradients influencing rice-arthropod traits, indicating that different rice arthropods have distinct habitat requirements. Whereas small parasitoids and species mostly present in the rice canopy were favored in landscapes with high compositional heterogeneity, predators and medium-sized species occupying the base of the rice plant, including planthoppers, mostly occurred in highly fragmented rice habitats. We demonstrate the importance of landscape heterogeneity as an ecological filter for rice arthropods, identifying how the different components of landscape heterogeneity selected for or against specific functional traits. However, the contrasting effects of landscape parameters on different groups of natural enemies indicate that not all beneficial rice arthropods can be promoted at the same time when using a single land management strategy. Increasing compositional heterogeneity in rice landscapes can promote parasitoids but may also negatively affect predators. Future research should focus on identifying trade-offs between fragmented rice habitats and structurally diverse landscapes to maximize the presence of multiple groups of beneficial arthropods.

Biodiversity in European agricultural landscapes: transformative societal changes needed

Reversing the decline of biodiversity in European agricultural landscapes is urgent. We suggest eight measures addressing politics, economics, and civil society to instigate transformative changes in agricultural landscapes. We emphasize the need for a well-informed society and political measures promoting sustainable farming by combining food production and biodiversity conservation.

Assumptions in ecosystem service assessments: Increasing transparency for conservation

Conservation efforts are increasingly supported by ecosystem service assessments. These assessments depend on complex multi-disciplinary methods, and rely on a number of assumptions which reduce complexity. If assumptions are ambiguous or inadequate, misconceptions and misinterpretations may arise when interpreting results of assessments. An interdisciplinary understanding of assumptions in ecosystem service science is needed to provide consistent conservation recommendations. Here, we synthesise and elaborate on 12 prevalent types of assumptions in ecosystem service assessments. These comprise conceptual and ethical foundations of the ecosystem service concept, assumptions on data collection, indication, mapping, and modelling, on socio-economic valuation and value aggregation, as well as about using assessment results for decision-making. We recommend future assessments to increase transparency about assumptions, and to test and validate them and their potential consequences on assessment reliability. This will support the taking up of assessment results in conservation science, policy and practice.

Identifying Agricultural Frontiers for Modeling Global Cropland Expansion

The increasing expansion of cropland is major driver of global carbon emissions and biodiversity loss. However, predicting plausible future global distributions of croplands remains challenging. Here, we show that, in general, existing global data aligned with classical economic theories of expansion explain the current (1992) global extent of cropland reasonably well, but not recent expansion (1992-2015). Deviations from models of cropland extent in 1992 ("frontierness") can be used to improve global models of recent expansion, most likely as these deviations are a proxy for cropland expansion under frontier conditions where classical economic theories of expansion are less applicable. Frontierness is insensitive to the land cover dataset used and is particularly effective in improving models that include mosaic land cover classes and the largely smallholder-driven frontier expansion occurring in such areas. Our findings have important implications as the frontierness approach offers a straightforward way to improve global land use change models.

Consequences of multiple imputation of missing standard deviations and sample sizes in meta-analysis

Meta-analyses often encounter studies with incompletely reported variance measures (e.g., standard deviation values) or sample sizes, both needed to conduct weighted meta-analyses. Here, we first present a systematic literature survey on the frequency and treatment of missing data in published ecological meta-analyses showing that the majority of meta-analyses encountered incompletely reported studies. We then simulated meta-analysis data sets to investigate the performance of 14 options to treat or impute missing SDs and/or SSs. Performance was thereby assessed using results from fully informed weighted analyses on (hypothetically) complete data sets. We show that the omission of incompletely reported studies is not a viable solution. Unweighted and sample size-based variance approximation can yield unbiased grand means if effect sizes are independent of their corresponding SDs and SSs. The performance of different imputation methods depends on the structure of the meta-analysis data set, especially in the case of correlated effect sizes and standard deviations or sample sizes. In a best-case scenario, which assumes that SDs and/or SSs are both missing at random and are unrelated to effect sizes, our simulations show that the imputation of up to 90% of missing data still yields grand means and confidence intervals that are similar to those obtained with fully informed weighted analyses. We conclude that multiple imputation of missing variance measures and sample sizes could help overcome the problem of incompletely reported primary studies, not only in the field of ecological meta-analyses. Still, caution must be exercised in consideration of potential correlations and pattern of missingness.

Deciphering the Biodiversity-Production Mutualism in the Global Food Security Debate

Without changes in consumption, along with sharp reductions in food waste and postharvest losses, agricultural production must grow to meet future food demands. The variety of concepts and policies relating to yield increases fail to integrate an important constituent of production and human nutrition - biodiversity. We develop an analytical framework to unpack this biodiversity-production mutualism (BPM), which bridges the research fields of ecology and agroeconomics and makes the trade-off between food security and protection of biodiversity explicit. By applying the framework, the incorporation of agroecological principles in global food systems are quantifiable, informed assessments of green total factor productivity (TFP) are supported, and possible lock-ins of the global food system through overintensification and associated biodiversity loss can be avoided.

Conventional land-use intensification reduces species richness and increases production: A global meta-analysis

Most current research on land-use intensification addresses its potential to either threaten biodiversity or to boost agricultural production. However, little is known about the simultaneous effects of intensification on biodiversity and yield. To determine the responses of species richness and yield to conventional intensification, we conducted a global meta-analysis synthesizing 115 studies which collected data for both variables at the same locations. We extracted 449 cases that cover a variety of areas used for agricultural (crops, fodder) and silvicultural (wood) production. We found that, across all production systems and species groups, conventional intensification is successful in increasing yield (grand mean + 20.3%), but it also results in a loss of species richness (-8.9%). However, analysis of sub-groups revealed inconsistent results. For example, small intensification steps within low intensity systems did not affect yield or species richness. Within high-intensity systems species losses were non-significant but yield gains were substantial (+15.2%). Conventional intensification within medium intensity systems revealed the highest yield increase (+84.9%) and showed the largest loss in species richness (-22.9%). Production systems differed in their magnitude of richness response, with insignificant changes in silvicultural systems and substantial losses in crop systems (-21.2%). In addition, this meta-analysis identifies a lack of studies that collect robust biodiversity (i.e. beyond species richness) and yield data at the same sites and that provide quantitative information on land-use intensity. Our findings suggest that, in many cases, conventional land-use intensification drives a trade-off between species richness and production. However, species richness losses were often not significantly different from zero, suggesting even conventional intensification can result in yield increases without coming at the expense of biodiversity loss. These results should guide future research to close existing research gaps and to understand the circumstances required to achieve such win-win or win-no-harm situations in conventional agriculture.

The Art of Scientific Performance

Humanity builds upon scientific findings, but the credibility of science might be at risk in a 'postfactual' era of advanced information technologies. Here we propose a systemic change for science, to turn away from a growth paradigm and to refocus on quality, characterized by curiosity, surprise, discovery, and societal relevance.

Do drivers of biodiversity change differ in importance across marine and terrestrial systems - Or is it just different research communities' perspectives?

Cross-system studies on the response of different ecosystems to global change will support our understanding of ecological changes. Synoptic views on the planet's two main realms, the marine and terrestrial, however, are rare, owing to the development of rather disparate research communities. We combined questionnaires and a literature review to investigate how the importance of anthropogenic drivers of biodiversity change differs among marine and terrestrial systems and whether differences perceived by marine vs. terrestrial researchers are reflected by the scientific literature. This included asking marine and terrestrial researchers to rate the relevance of different drivers of global change for either marine or terrestrial biodiversity. Land use and the associated loss of natural habitats were rated as most important in the terrestrial realm, while the exploitation of the sea by fishing was rated as most important in the marine realm. The relevance of chemicals, climate change and the increasing atmospheric concentration of CO₂ were rated differently for marine and terrestrial biodiversity respectively. Yet, our literature review provided less evidence for such differences leading to the conclusion that while the history of the use of land and sea differs, impacts of global change are likely to become increasingly similar.

Water Quality Is a Poor Predictor of Recreational Hotspots in England

Maintaining and improving water quality is key to the protection and restoration of aquatic ecosystems, which provide important benefits to society. In Europe, the Water Framework Directive (WFD) defines water quality based on a set of biological, hydro-morphological and chemical targets, and aims to reach good quality conditions in all river bodies by the year 2027. While recently it has been argued that achieving these goals will deliver and enhance ecosystem services, in particular recreational services, there is little empirical evidence demonstrating so. Here we test the hypothesis that good water quality is associated with increased utilization of recreational services, combining four surveys covering walking, boating, fishing and swimming visits, together with water quality data for all water bodies in eight River Basin Districts (RBDs) in England. We compared the percentage of visits in areas of good water quality to a set of null models accounting for population density, income, age distribution, travel distance, public access, and substitutability. We expect such association to be positive, at least for fishing (which relies on fish stocks) and swimming (with direct contact to water). We also test if these services have stronger association with water quality relative to boating and walking alongside rivers, canals or lakeshores. In only two of eight RBDs (Northumbria and Anglian) were both criteria met (positive association, strongest for fishing and swimming) when comparing to at least one of the null models. This conclusion is robust to variations in dataset size. Our study suggests that achieving the WFD water quality goals may not enhance recreational ecosystem services, and calls for further empirical research on the connection between water quality and ecosystem services.

Simulation of forest tree species' bud burst dates for different climate scenarios: chilling requirements and photo-period may limit bud burst advancement

This study investigates whether the assumed increase of winter and spring temperatures is depicted by phenological models in correspondingly earlier bud burst (BB) dates. Some studies assume that rising temperatures lead to an earlier BB, but even later BB has been detected. The phenological model PIM (promoter-inhibitor-model) fitted to the extensive phenological database of the German Weather Service was driven by several climate scenarios. This model accounts for the complicated mechanistic interactions between chilling requirements, temperature and photo-period. It predicts BB with a r ² between 0.41 and 0.62 and a RMSE of around 1 week, depending on species. Parameter sensitivities depict species dependent interactions between growth and chilling requirements as well as photo-period. A mean trend to earlier BB was revealed for the period 2002- 2100, varying between -0.05 and -0.11 days per year, depending on species. These trends are lower than for the period 1951- 2009. Within climate scenario period, trends are decreasing for beech and chestnut, stagnating for birch and increasing for oak. Results suggest that not fulfilled chilling requirements accompanied by an increasing dependency on photo-period potentially limit future BB advancement. The combination of a powerful phenological model, a large scale phenological database and several climate scenarios, offers new insights into the mechanistic comprehension of spring phenology.

Uncertainty of Monetary Valued Ecosystem Services - Value Transfer Functions for Global Mapping

Growing demand of resources increases pressure on ecosystem services (ES) and biodiversity. Monetary valuation of ES is frequently seen as a decision-support tool by providing explicit values for unconsidered, non-market goods and services. Here we present global value transfer functions by using a meta-analytic framework for the synthesis of 194 case studies capturing 839 monetary values of ES. For 12 ES the variance of monetary values could be explained with a subset of 93 study- and site-specific variables by utilizing boosted regression trees. This provides the first global quantification of uncertainties and transferability of monetary valuations. Models explain from 18% (water provision) to 44% (food provision) of variance and provide statistically reliable extrapolations for 70% (water provision) to 91% (food provision) of the terrestrial earth surface. Although the application of different valuation methods is a source of uncertainty, we found evidence that assuming homogeneity of ecosystems is a major error in value transfer function models. Food provision is positively correlated with better life domains and variables indicating positive conditions for human well-being. Water provision and recreation service show that weak ownerships affect valuation of other common goods negatively (e.g. non-privately owned forests). Furthermore, we found support for the shifting baseline hypothesis in valuing climate regulation. Ecological conditions and societal vulnerability determine valuation of extreme event prevention. Valuation of habitat services is negatively correlated with indicators characterizing less favorable areas. Our analysis represents a stepping stone to establish a standardized integration of and reporting on uncertainties for reliable and valid benefit transfer as an important component for decision support.

Harmonizing Biodiversity Conservation and Productivity in the Context of Increasing Demands on Landscapes

Biodiversity conservation and agricultural production are often seen as mutually exclusive objectives. Strategies for reconciling them are intensely debated. We argue that harmonization between biodiversity conservation and crop production can be improved by increasing our understanding of the underlying relationships between them. We provide a general conceptual framework that links biodiversity and agricultural production through the separate relationships between land use and biodiversity and between land use and production. Hypothesized relationships are derived by synthesizing existing empirical and theoretical ecological knowledge. The framework suggests nonlinear relationships caused by the multifaceted impacts of land use (composition, configuration, and intensity). We propose solutions for overcoming the apparently dichotomous aims of maximizing either biodiversity conservation or agricultural production and suggest new hypotheses that emerge from our proposed framework.

Meta-studies in land use science: Current coverage and prospects

Land use science has traditionally used case-study approaches for in-depth investigation of land use change processes and impacts. Meta-studies synthesize findings across case-study evidence to identify general patterns. In this paper, we provide a review of meta-studies in land use science. Various meta-studies have been conducted, which synthesize deforestation and agricultural land use change processes, while other important changes, such as urbanization, wetland conversion, and grassland dynamics have hardly been addressed. Meta-studies of land use change impacts focus mostly on biodiversity and biogeochemical cycles, while meta-studies of socioeconomic consequences are rare. Land use change processes and land use change impacts are generally addressed in isolation, while only few studies considered trajectories of drivers through changes to their impacts and their potential feedbacks. We provide a conceptual framework for linking meta-studies of land use change processes and impacts for the analysis of coupled human-environmental systems. Moreover, we provide suggestions for combining meta-studies of different land use change processes to develop a more integrated theory of land use change, and for combining meta-studies of land use change impacts to identify tradeoffs between different impacts. Land use science can benefit from an improved conceptualization of land use change processes and their impacts, and from new methods that combine meta-study findings to advance our understanding of human-environmental systems.

A new multiscale approach for monitoring vegetation using remote sensing-based indicators in laboratory, field, and landscape

Remote sensing is an important tool for studying patterns in surface processes on different spatiotemporal scales. However, differences in the spatiospectral and temporal resolution of remote sensing data as well as sensor-specific surveying characteristics very often hinder comparative analyses and effective up- and downscaling analyses. This paper presents a new methodical framework for combining hyperspectral remote sensing data on different spatial and temporal scales. We demonstrate the potential of using the "One Sensor at Different Scales" (OSADIS) approach for the laboratory (plot), field (local), and landscape (regional) scales. By implementing the OSADIS approach, we are able (1) to develop suitable stress-controlled vegetation indices for selected variables such as the Leaf Area Index (LAI), chlorophyll, photosynthesis, water content, nutrient content, etc. over a whole vegetation period. Focused laboratory monitoring can help to document additive and counteractive factors and processes of the vegetation and to correctly interpret their spectral response; (2) to transfer the models obtained to the landscape level; (3) to record imaging hyperspectral information on different spatial scales, achieving a true comparison of the structure and process results; (4) to minimize existing errors from geometrical, spectral, and temporal effects due to sensor- and time-specific differences; and (5) to carry out a realistic top- and downscaling by determining scale-dependent correction factors and transfer functions. The first results of OSADIS experiments are provided by controlled whole vegetation experiments on barley under water stress on the plot scale to model LAI using the vegetation indices Normalized Difference Vegetation Index (NDVI) and green NDVI (GNDVI). The regression model ascertained from imaging hyperspectral AISA-EAGLE/HAWK (DUAL) data was used to model LAI. This was done by using the vegetation index GNDVI with an R (2) of 0.83, which was transferred to airborne hyperspectral data on the local and regional scales. For this purpose, hyperspectral imagery was collected at three altitudes over a land cover gradient of 25 km within a timeframe of a few minutes, yielding a spatial resolution from 1 to 3 m. For all recorded spatial scales, both the LAI and the NDVI were determined. The spatial properties of LAI and NDVI of all recorded hyperspectral images were compared using semivariance metrics derived from the variogram. The first results show spatial differences in the heterogeneity of LAI and NDVI from 1 to 3 m with the recorded hyperspectral data. That means that differently recorded data on different scales might not sufficiently maintain the spatial properties of high spatial resolution hyperspectral images.

Spatial and temporal trends of global pollination benefit

Pollination is a well-studied and at the same time a threatened ecosystem service. A significant part of global crop production depends on or profits from pollination by animals. Using detailed information on global crop yields of 60 pollination dependent or profiting crops, we provide a map of global pollination benefits on a 5' by 5' latitude-longitude grid. The current spatial pattern of pollination benefits is only partly correlated with climate variables and the distribution of cropland. The resulting map of pollination benefits identifies hot spots of pollination benefits at sufficient detail to guide political decisions on where to protect pollination services by investing in structural diversity of land use. Additionally, we investigated the vulnerability of the national economies with respect to potential decline of pollination services as the portion of the (agricultural) economy depending on pollination benefits. While the general dependency of the agricultural economy on pollination seems to be stable from 1993 until 2009, we see increases in producer prices for pollination dependent crops, which we interpret as an early warning signal for a conflict between pollination service and other land uses at the global scale. Our spatially explicit analysis of global pollination benefit points to hot spots for the generation of pollination benefits and can serve as a base for further planning of land use, protection sites and agricultural policies for maintaining pollination services.

Simulating Demography and Housing Demand in an Urban Region under Scenarios of Growth and Shrinkage

After the fall of the Berlin wall in 1989, demographic decline and urban shrinkage brought massive changes in the housing stock in East German cities. Urban planners and policy makers face complex problems caused by the resulting vacancies and demolitions and the handling of urban brownfields in the inner city. At the same time, cities are under ongoing pressure of suburbanisation. Because existing models focus mainly on demographic and urban growth and their impact on housing stocks, we present a simulation model that is able to compute both growth and shrinkage processes. We uncover nonlinear dynamics and feedbacks between demography, housing preference, and supply of housing space. The simulation results show that, despite population decline, the increasing number of single households leads to a growing total housing demand in the central parts of the study area. Beyond this area, residential vacancies in multistorey housing segments will remain regardless of population growth. At the same time, the simulations show that, despite population shrinkage and an overall oversupply of flats, there is a negative net demand for flats in affordable prefabricated housing estates as the percentage of low-income households increases. These findings will help planners modify or adapt their visions of the residential function in shrinking cities and to adjust current programmes of renewal and restructuring.

How can we make progress with decision support systems in landscape and river basin management? Lessons learned from a comparative analysis of four different decision support systems

This article analyses the benefits and shortcomings of the recently developed decision support systems (DSS) FLUMAGIS, Elbe-DSS, CatchMODS, and MedAction. The analysis elaborates on the following aspects: (i) application area/decision problem, (ii) stakeholder interaction/users involved, (iii) structure of DSS/model structure, (iv) usage of the DSS, and finally (v) most important shortcomings. On the basis of this analysis, we formulate four criteria that we consider essential for the successful use of DSS in landscape and river basin management. The criteria relate to (i) system quality, (ii) user support and user training, (iii) perceived usefulness and (iv) user satisfaction. We can show that the availability of tools and technologies for DSS in landscape and river basin management is good to excellent. However, our investigations indicate that several problems have to be tackled. First of all, data availability and homogenisation, uncertainty analysis and uncertainty propagation and problems with model integration require further attention. Furthermore, the appropriate and methodological stakeholder interaction and the definition of 'what end-users really need and want' have been documented as general shortcomings of all four examples of DSS. Thus, we propose an iterative development process that enables social learning of the different groups involved in the development process, because it is easier to design a DSS for a group of stakeholders who actively participate in an iterative process. We also identify two important lines of further development in DSS: the use of interactive visualization tools and the methodology of optimization to inform scenario elaboration and evaluate trade-offs among environmental measures and management alternatives.

Omnipresent Sprawl? A Review of Urban Simulation Models with Respect to Urban Shrinkage

Simulation models on urban land-use change help in understanding urban systems and assist in urban planning. One of the challenges of simulating urban regions in Europe as well as in North America or Japan is urban shrinkage, where deindustrialisation, massive population losses, and ageing cause unforeseen (or unexpected) commercial and housing vacancies in cities. In order to set up a conceptual framework for model improvement to assist such challenges, we review recent urban land-use-change simulation models, using four different modelling approaches: system dynamics, linked transport — urban models, cellular automata, and agent-based modelling. The focus of the review is to assess the causalities and feedback mechanisms that were implemented in these models. The results show that simulation models are very heterogeneous in implemented mechanisms leading to urban land-use dynamics. No single model fulfils all of the criteria required to model urban shrinkage in a spatially explicit way. However, system-dynamic models that are documented in the literature can serve as a good starting point for spatially nonexplicit simulation, and one example was found for linked transport — urban models which encompasses aspects of urban shrinkage. The potential of cellular automata is unclear as spatially explicit data on vacancies to feed this class of models is usually not available. Agent-based models appear to be the most promising approach for spatially explicit modelling of urban shrinkage.

Spatially explicit modelling of transgenic maize pollen dispersal and cross-pollination

Modelling of pollen dispersal and cross-pollination is of great importance for the ongoing discussion on thresholds for the adventitious presence of genetically modified material in food and feed. Two different modelling approaches for pollen dispersal are used to simulate the cross-pollination rate of pollen emerged from an adjacent transgenic crop field. The models are applied to cross-pollination data from field experiments with transgenic maize (Zea mays). The data were generated by an experimental setup specifically designed to suit the demands of mathematical modelling. First a Gaussian plume model is used for the simulation of pollen transport in and from plant canopies. This is a semiempirical approach combining the atmospheric diffusion equation and Lagrangian methodology. The second model is derived from the localised near field (LNF) theory and based on the physical processes in the canopy. Both modelling approaches prove to be appropriate for the simulation of the cross-pollination rates at distances of about 7.5m and more from the transgene source. The simulation of the cross-pollination rate is less precise at the edge of the source plot especially with the LNF theory. However, the simulation results lie within the range of variability of the observations. Concluding can be pointed out that both models might be adapted to other pollen dispersal experiments of different crops and plot sizes.

Modelling approaches to compare sorption and degradation of metsulfuron-methyl in laboratory micro-lysimeter and batch experiments

Results of laboratory batch studies often differ from those of outdoor lysimeter or field plot experiments--with respect to degradation as well as sorption. Laboratory micro-lysimeters are a useful device for closing the gap between laboratory and field by both including relevant transport processes in undisturbed soil columns and allowing controlled boundary conditions. In this study, sorption and degradation of the herbicide metsulfuron-methyl in a loamy silt soil were investigated by applying inverse modelling techniques to data sets from different experimental approaches under laboratory conditions at a temperature of 10 degrees C: first, batch-degradation studies and, second, column experiments with undisturbed soil cores (28 cm length x 21 cm diameter). The column experiments included leachate and soil profile analysis at two different run times. A sequential extraction method was applied in both study parts in order to determine different binding states of the test item within the soil. Data were modelled using ModelMaker and Hydrus-1D/2D. Metsulfuron-methyl half-life in the batch-experiments (t1/2 = 66 days) was shown to be about four times higher than in the micro-lysimeter studies (t1/2 about 17 days). Kinetic sorption was found to be a significant process both in batch and column experiments. Applying the one-rate-two-site kinetic sorption model to the sequential extraction data, it was possible to associate the stronger bonded fraction of metsulfuron-methyl with its kinetically sorbed fraction in the model. Although the columns exhibited strong significance of multi-domain flow (soil heterogeneity), the comparison between bromide and metsulfuron-methyl leaching and profile data showed clear evidence for kinetic sorption effects. The use of soil profile data had significant impact on parameter estimates concerning sorption and degradation. The simulated leaching of metsulfuron-methyl as it resulted from parameter estimation was shown to decrease when soil profile data were considered in the parameter estimation procedure. Moreover, it was shown that the significance of kinetic sorption can only be demonstrated by the additional use of soil profile data in parameter estimation. Thus, the exclusive use of efflux data from leaching experiments at any scale can lead to fundamental misunderstandings of the underlying processes.