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• 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]

Climate-smart interventions in agriculture have varying costs and environmental and economic impacts. Their implementation requires appropriate investment decisions by policy makers that are relevant for current as well as future scenarios of agro-ecology, climate and economic development. Decision support tools are therefore needed to assist different stakeholders to prioritize and hence implement appropriate strategic interventions. These interventions transform agriculture ecosystems to climate-resilient, adaptive and efficient. This paper outlines the mathematical modelling framework of one such, the Climate Smart Agricultural Prioritization (CSAP) toolkit. This toolkit employs a dynamic, spatially-explicit multi-objective optimization model to explore a range of agricultural growth pathways coupled with climate-adaptation strategies to meet agricultural development and environmental goals. The toolkit consists of three major components: (i) land evaluation including assessment of resource availability, land suitability, yield and input-output estimation for all promising crop production practices and technologies for key agro-ecological units; (ii) formulation of scenarios based on policy views and development plans; and (iii) land-use optimization in the form of linear programming models. Climate change and socio-economic drivers condition the land evaluation, technological input-output relations, and specification of optimization objectives that define modelled scenarios. By integrating detailed bottom-up biophysical, climate impact and agricultural-emissions models, CSAP is capable of supporting multi-objective analysis of agricultural production goals in relation to food self-sufficiency, incomes, employment and mitigation targets, thus supporting a wide range of analyses ranging from food security assessment to environmental impact assessment to preparation of climate smart development plans. [ABSTRACT FROM AUTHOR]

Promoting urban transition is critical, particularly for China’s rapid urbanization. Circular economy strategy is widely recognized as an effective way to achieve a low-carbon transition of cities through improved waste recycling and industrial symbiosis. However, the evidence of low-carbon benefit is less reported. While the carbon footprint (CFP) represents a mature tool responding to climate change concerns, limited studies have made use of CFP as a proxy for the performance of urban circular economy promotion. The aim of this paper is to investigate the CFP with a ten years span (2002–2012) of Guiyang, so as to understand how its circular economy practices have led to low-carbon benefits. Guiyang, one of China’s national pilots of the Circular Economy (CE) City, the Low-Carbon City, as well as the Ecological Civilization City, has offered an ideal laboratory where the opportunities and challenges for a low-carbon urban transition can be explicitly discussed. A hybrid model that integrates an input-output (IO) approach and process-based inventory analysis is developed to distinguish between direct carbon emissions of sectors from energy consumption, and indirect carbon emissions related to upstream and downstream flows both from production and consumption perspectives. The CFP of Guiyang in 2002, 2007 (after becoming the circular economy pilot) and 2012 (with implementation of urban industrial symbiosis) are analyzed by taking the 2002 as year of business as usual (BAU) scenario. Particularly, we identify scenarios related to proposed urban industrial symbiosis. Results imply that dramatic resource saving and CFP reductions could be achieved simultaneously. Changes to the CFP in 2002, 2007 and 2012 provide critical insights into the role of circular economy in speeding up urban transition towards a low-carbon society. Finally, policy recommendations to tackle the barriers to regional low-carbon transition are proposed. We believe that this study is informative for policy makers of urban planning by shedding a light on innovative eco-industrial development and urban transition in China. [ABSTRACT FROM AUTHOR]

• The environmental policy impact climate (EPIC) model is capable of simulating crop productivity for hundreds of years under a variety of climate scenarios, environmental conditions, and management systems and is often used to study the impact of climate change on agriculture. • The application of EPIC crop growth model can be divided into three categories: the improvement of crop growth model accuracy, the applied research on the effects of climate change, and the mitigation of agricultural drought risk. • Four aspects of research hot spots in the future: applications of extreme climate change, application of agricultural drought risk assessment and early warning, application of an agricultural catastrophe management system, application of drought relief operations. Climate change is breaking global food supplies. The Environmental Policy Impact Climate (EPIC) model is capable of simulating crop productivity for hundreds of years under a variety of climate scenarios, environmental conditions, and management systems and is often used to study the impact of climate change on agriculture. Combined with systematic evaluation and meta-analysis, this paper reviews the application of EPIC crop growth model. Those research can be divided into three categories: the improvement of crop growth model accuracy, the applied research on the effects of climate change, and the mitigation of agricultural drought risk. In addition, the paper puts forward four aspects of research hot spots in the future: applications of extreme climate change, application of agricultural drought risk assessment and early warning, application of an agricultural catastrophe management system, application of drought relief operations. Research shows the accuracy of the model is greatly improved by determining the genetic parameters of crops, modifying the parameters according to the weather, land use and technical conditions, and establishing the local and even global model parameter database; the uncertainty of model parameters, input data and model structure is analyzed to improve the stability of the model. In the process of discussing the influence mechanism and sensitivity analysis of climate change on crop growth, the analysis of various meteorological factors is concentrated from multi-factor analysis to temperature and precipitation. With the development of computing technology, the emergence of GIS technology and GCMs technology makes it possible to predict the impact of different meteorological factors on crop growth under future climate change. In some studies, EPIC crop growth model has been performed to reduce the risk of agricultural drought. [ABSTRACT FROM AUTHOR]

Abstract: Modeling of global climate change is moving from global circulation model (GCM)-type projections with coupled biogeochemical models to projections of ecological responses, including food web and upper trophic levels. Marine and coastal ecosystems are highly susceptible to the impacts of global climate change and also produce significant ecosystem services. The effects of global climate change on coastal and marine ecosystems involve a much wider array of effects than the usual temperature, sea level rise, and precipitation. This paper is an overview for a collection of 12 papers that examined various aspects of global climate change on marine ecosystems and comprise this special issue. We summarized the major features of the models and analyses in the papers to determine general patterns. A wide range of ecosystems were simulated using a diverse set of modeling approaches. Models were either 3-dimensional or used a few spatial boxes, and responses to global climate change were mostly expressed as changes from a baseline condition. Three issues were identified from the across-model comparison: (a) lack of standardization of climate change scenarios, (b) the prevalence of site-specific and even unique models for upper trophic levels, and (c) emphasis on hypothesis evaluation versus forecasting. We discuss why these issues are important as global climate change assessment continues to progress up the food chain, and, when possible, offer some initial steps for going forward. [Copyright &y& Elsevier]

A wide range of insects affect crop production and cause considerable yield losses. Difficulties reside on the development and adaptation of adequate strategies to predict insect pests for their timely management to ensure enhanced agricultural production. Several conceptual modelling frameworks have been proposed, and the choice of an approach depends largely on the objective of the model and the availability of data. This paper presents a summary of decades of advances in insect population dynamics, phenology models, distribution and risk mapping. Existing challenges on the modelling of insects are listed; followed by innovations in the field. New approaches include artificial neural networks, cellular automata (CA) coupled with fuzzy logic (FL), fractal, multi-fractal, percolation, synchronization and individual/agent-based approaches. A concept for assessing climate change impacts and providing adaptation options for agricultural pest management independently of the United Nations Intergovernmental Panel on Climate Change (IPCC) emission scenarios is suggested. A framework for estimating losses and optimizing yields within crop production system is proposed and a summary on modelling the economic impact of pests control is presented. The assessment shows that the majority of known insect modelling approaches are not holistic; they only concentrate on a single component of the system, i.e. the pest, rather than the whole crop production system. We suggest system thinking as a possible approach for linking crop, pest, and environmental conditions to provide a more comprehensive assessment of agricultural crop production. [ABSTRACT FROM AUTHOR]

Modelling hydraulic functioning of a forest stand is a prerequisite to predict the future impact of climate change on forests. In this paper, we used a process-based model of the soil-plant-atmosphere continuum to investigate the links between energy budget and water balance, and to emphasize the key processes involved in the control of transpiration and water status of forest trees. The model describes stomatal conductance as a function of photosynthesis, intercellular CO 2 concentration and leaf water potential. The latter in turn depends on soil and tree storage water potentials, the water flux through the soil and the trees, hydraulic resistances and stomatal conductance. We have implemented in the model a detailed tree water storage scheme, canopy interception of precipitation, and the rate of change of forest canopy energy storage. In this model, physical climate processes and ecological processes are closely coupled which involves important interactions and feedbacks. The model reproduces the observed variation in leaf water potential in dry and wet conditions. It successfully captures the decrease in soil water content under both non-limiting soil water and severe drought conditions and there is a good agreement between measured and simulated sensible and latent heat fluxes throughout the season. Simulations also show that significant amounts of intercepted water can be lost through evaporation during rain events. The results corroborate that the concept of hydraulic capacitance provides a simple and effective means of simulating the buffering action of tree water storage on tree water status. The two key parameters that control transpiration and water status of the trees are xylem hydraulic resistances and sensitivity of stomata to leaf water potential. The results confirm that stomatal conductance cannot be modelled using leaf-level processes alone, but must be incorporated into a comprehensive model of water flow from soil through the plants to the atmosphere where various self-regulation are set up to ensure a complete water status equilibrium. [ABSTRACT FROM AUTHOR]

Abstract: Species distribution models (SDMs) are increasingly used to forecast impacts of climate change on species geographic distributions, but the reliability of predictions is scrutinized. The main limitation of SDMs lies in their assumption that species’ ranges are determined mostly by climate, which is arguable. For instance, biotic interactions, habitat and elevation may affect species ranges. The inclusion of habitat-related variables as predictors in SDMs is generally accepted, but there is no consensus regarding the inclusion of elevation. A review of randomly chosen literature revealed that elevation is used as a predictor variable by just over half of the papers studied with no apparent trends as to why, except that papers predicting mammal species distributions for large regions included elevation more often than not, and that papers that predicted mammal ranges for small regions tended to exclude elevation. In addition, we compared the performance of SDMs with and without elevation as a predictor variable for the distribution of north European mammals and plants and found that the difference between their performances is statistically significant for mammals, slightly favouring exclusion of elevation. No differences were found for plants. [Copyright &y& Elsevier]

Increasing global temperatures as a result of climate change are widely considered inevitable for Australia. Despite this, the specific effects of climate change on Australian agriculture are little studied and the effects on agricultural pests and diseases are virtually unknown. In this paper we consider the impact of climate change on the Asiatic citrus psyllid (Diaphorina citri Kuwayama [Hemiptera: Psyllidae]); one of two known vectors of huanglongbing (citrus greening); a debilitating disease which is caused in Asia by a phloem-limited bacterium ‘Candidatus Liberibacter asiaticus’ (α-Proteobacteria). D. citri does not occur in Australia, but if introduced would pose a major threat to the viability of the Australian citrus industry and to native Citrus species. This paper presents an approach developed to understand how climate change may influence the behaviour, distribution and breeding potential of D. citri. Here we developed and describe an initial dynamic point model of D. citri biology in relation to its citrus host and applied it to a scenario of increasing temperatures, as indicators of climate change, on a continental scale. A comparison between model outputs for the three time frames considered (1990, 2030 and 2070) confirms that increasing temperatures projected under climate change will affect the timing and duration of new citrus growth (flush) necessary for psyllid development throughout Australia. Flushing will start progressively earlier as the temperature increases and be of shorter duration. There will also be a gradual southward expansion of shorter durations of the occurrence of flush. Increasing temperatures will impact on D. citri both directly through alteration of its temperature dependant development cycle and indirectly through the impact on the host flushing cycle. For the whole of Australia, a comparison between model outputs for the three scenarios considered indicates the seasonality of D. citri development will change to match changes in citrus flush initiation. Results indicate that the risk of establishment by D. citri is projected to decrease under increasing temperatures, mainly due to shortened intervals when it can feed on new leaf flushes of the host. However, the spatially heterogeneous results also suggest that regions located on the southern coastline of Australia could become more suitable for D. citri than projected under current temperatures. These results confirm the value of a linked host-pest approach as based on D. citri climatic requirements alone the model would have accounted only for shorter development periods and predicted an increased risk of potential distribution. [Copyright &y& Elsevier]

In their efforts to deal with global climate change, scientists and governments have given much attention to the carbon emissions associated with fossil fuels and to strategies for reducing their use. While it is very important to burn less fossil fuel and to employ alternative energy sources, other carbon-reduction options must also be considered. Given that forests comprise a large portion of the global landbase and that they play a very significant role in the global carbon cycle, it is logical to examine how forest management practices could effect reductions in carbon emissions. Many papers that discuss forest carbon sinks or sources refer only to the short term (<20 years). This paper focuses on the sustainable carbon storage contributions of a forest over the long term. This paper explains that long-term carbon storage and reduced carbon fluctuation can be achieved by a combination of improved forest management and efficient transfer of carbon into wood products. Here we show how three different forest management scenarios affect the overall carbon storage capacity of forest and wood products combined over the long term. We used a timber supply model and scenario analysis to predict forest carbon and other resource conditions over time in the Prince George Forest District, a 3.4-million-ha landbase in northern British Columbia. We found that the high-harvest scenario stores 3% more carbon than the low-harvest scenario and 27% (120milliontonnes) more carbon than the no-harvest scenario even though only 1.2-millionha is in timber harvesting landbase. Our results tell us that forest management practices that maintain and increase forest area, reduce natural disturbances in the forest, improve forest conditions, and ensure the appropriate and timely transfer of carbon into wood products lead to increasing overall carbon storage, thereby reducing carbon in the atmosphere. [Copyright &y& Elsevier]

• A regional model for algal bloom frequency under a non-stationary framework. • A model to project bloom scenarios in response to anthropogenic developments. • A model to test the effects of mitigation scenarios on bloom frequency. • Case studies testing model capacity to foresight climate change impacts on blooms. • Projections showing doubling of bloom frequency in a eutrophic lake over next 80 years. There are significant social, economic and environmental costs related to phytoplankton blooms, particularly when they are dominated by cyanobacteria. However, the climatic, physiographic and morphological drivers involved in bloom phenology create complex spatiotemporal patterns in biomass and taxonomic composition, making monitoring and forecasting challenging. This complexity also limits the ability of process-based algorithms to model bloom phenology. Stochastic approaches and predictive modelling are valuable risk management tools for natural events. This paper presents a novel model to estimate the conditional density of phytoplankton blooms based on environmental covariates, namely the physiography and climate descriptors of lake watersheds. The goal was to provide a tool to project future scenarios of bloom phenology in response to climate change and anthropogenic developments, and to test the efficiency of mitigating approaches. Specifically, we developed a non-stationary regional model to estimate the probability of bloom occurrence in 580 lakes in Quebec, Canada, and to study the impact of physiographic or climate changes over our reference period and future horizons. Results show that the regional model is more precise and accurate than local models, which are usually used in frequency analyses of extreme values. Simulations conducted for different climatic and physiographic scenarios on the watershed of Lake Brome indicate a 65% increase in bloom frequency on the horizon of years 2080–2100. The model also evaluates the effects of different environmental disturbances on bloom phenology, such as agriculture or settlement increase in the watershed. Disturbance case studies were carried out on lakes with different trophic levels, physiographic and climatic characteristics. Simulations indicate that bloom frequency in a highly urbanized meso-eutrophic lake (Lake Brome) and a eutrophic lake with a watershed dominated by agriculture (Missisquoi Bay) would be more responsive to changes in climate rather than land cover, although physiographic disturbances would still generate considerable effects. A meso-eutrophic lake with a watershed dominated by forest (Lake Aylmer) would neither respond to increases in agriculture nor settlement but would be most affected by increases in degree days. Another scenario focused on a highly forested oligo-mesotrophic lake (Lake St. Jean). Bloom phenology in this lake was most affected by disturbances in agriculture or settlement land use. Overall, these results suggest that lakes with a high level of eutrophication are the least responsive to the pressures resulting from land cover changes. [Display omitted] [ABSTRACT FROM AUTHOR]

Marine and coastal ecosystems provide multiple benefits that are fundamental to human wellbeing, but human actions are disrupting and impacting the Earth's ecosystems at an alarming rate. The Ecopath approach was designed to understand the impact of the wide range of anthropogenic pressures that are exerted on the oceans, and of management options for countering these, and it has over the last thirty years grown into a complex and capable modelling framework: “Ecopath with Ecosim” – with Ecospace added on. Exciting new developments of the approach are contributing to address critical and complex issues related to the health of marine ecosystems such as invasion of species, illegal fishing activities, climate change and the development of new activities (e.g., aquaculture and infrastructure development) in coastal areas. This Special Issue presents new findings from selected case studies around the world using advanced features of Ecopath with Ecosim that were presented at the International Conference “Ecopath 30 Years-Modelling ecosystem dynamics: beyond boundaries with EwE” (November 2014, Barcelona, Spain). These contributions showcase new capabilities and findings of Ecopath with Ecosim models applications. Together these papers show that a range of diverse ecological, economic, social and governance drivers are often interacting at dynamic and temporal scales to modify marine resources, which underlines that managing marine ecosystems needs a continuous effort to integrate multiple processes. [ABSTRACT FROM AUTHOR]

Recent decades have seen great advances in ecological modelling and computing power, enabling ecologists to build increasingly detailed models to more accurately represent ecological systems. To better inform environmental decision-making, it is important that the predictions of these models are expressed in simple ways that are straightforward for stakeholders to comprehend and use. One way to achieve this is to predict threshold values for environmental perturbations (e.g. climate change, habitat modification, food loss, sea level rise) associated with negative impacts on individuals, populations, communities or ecosystems. These thresholds can be used by stakeholders to inform management and policy. In this paper we demonstrate how this approach can use individual-based models of birds, their prey and habitats, to provide the evidence-base for coastal bird conservation and shellfishery management. In particular, we show how such models can be used to identify threshold values for perturbations of food abundance that can impact negatively on bird populations. We highlight how environmental thresholds could be used more widely to inform management of species and habitats under environmental change. [ABSTRACT FROM AUTHOR]

Predicting cumulative effects is an important challenge of theoretical and management ecology. If a population will be exposed to multiple stressors (e.g., toxins, introduced competitors, climate change), will their cumulative effects be independent and hence multiplicative (the population survival rates due to each stressor can be multiplied together to determine the total reduction in abundance), synergistic (cumulative effects are greater than multiplicative), or antagonistic (stressors offset each other so cumulative effects are less than multiplicative)? Further, the effects of each stressor can vary with such factors as habitat quality, population density, and weather. It is difficult to predict cumulative effects with traditional population-level models because such models must assume the type and strength of stressor interactions a priori , and measuring stressor effects and interactions empirically is rarely practical. Instead, we used an individual-based model in which cumulative effects emerge from how each stressor affects the growth and survival of individuals, and how individuals interact. Our model is in fact based on theoretical concepts explored in the landmark 1980 paper of DeAngelis et al. (Cannibalism and size dispersal in young-of-the-year largemouth bass: experiment and model, Ecol. Model. 8, 133–148): in a community of fish that eat each other, initial differences in size among individuals have strong effects on subsequent abundance and size distributions. We model survival and growth of juvenile Colorado pikeminnow ( Ptychocheilus lucius ) during their first year, and two stressors they are subject to. The first stressor is a daily cycle of flow fluctuations imposed by an upstream hydroelectric dam; these fluctuations affect habitat area, food supply, and temperature, which then affect juvenile fish growth. Second is an introduced fish species that competes with pikeminnow for food, while both species can prey on each other via the size-based mechanism described by DeAngelis et al. We simulated the effects of the 36 combinations of six levels of these two stressors in each of 28 sites and weather year to produce 840 scenarios, using 7 weather year datasets as replicates. Emergent cumulative effects were multiplicative in 69% of these scenarios, synergistic in 22%, and antagonistic in 9%. Therefore, any a priori assumption about stressor interactions would be wrong in many situations. Synergistic effects were most common in deeper and larger habitats favorable to the introduced species; antagonistic effects were most common in smaller habitats where the introduced species had low growth, because flow fluctuations further reduced the small food supply. [ABSTRACT FROM AUTHOR]

Virtual species are simplified models of real species that codify the response of those species to the climatic conditions. Virtual species have been used to quantify the response of species to climatic changes, to predict potential shifts in species’ geographic ranges and to test the methods used to predict the geographic ranges of species (Ecological Niche Models). Today, there are different methods used to construct virtual species for biogeographic analysis. All of those methods combine partial suitabilities across variables (temperature, precipitation, etc.) to create one multi-variable habitat suitability index. The normal procedure for combining partial suitabilities is to sum or multiply the individual layers. However, this procedure might yield misleading results. In this paper, we run several analyses that indicate that those methods underestimate the role of the limitation factors (factors with unsuitable conditions that should decrease the habitat suitability index to zero). To solve this problem we programmed an algorithm—the Niche Limitation algorithm (NicheLim). NicheLim uses the same philosophy as the BIOCLIM model: species have independent physiological tolerances to the environmental variables. This means that we must first transform each continuous layer into a presence-absence variable, and then combine them. Here, we discuss the current framework for constructing virtual species in biogeography and its main drawbacks. We then explain the improvements of using NicheLim to create our virtual species and test biogeographic hypotheses. [ABSTRACT FROM AUTHOR]

Many uncertainties emerge dealing with future climate conditions and their possible impacts on forests. In this paper we suggest a probabilistic approach extending existing ensemble species distribution modeling concepts by addressing important sources of uncertainty. We exemplify our approach using European beech as the target tree species and bioclimatic predictors derived from WorldClim data. Model parameter uncertainty is represented by 1000 parameter samples from a Bayesian generalized linear model. Climate change impact ( CCI ) is based on 63 different climate model outputs using four RCP-scenarios ( R epresentative C oncentration P athways) in addition to the parameter uncertainty. The proposed difference of the probability of occurrence p occ to a predefined threshold, allows for evaluation of parameter uncertainty as well as for the uncertainty of future climate and describes the changing niche position. Further, we suggest the probability that the probability of occurrence exceeds a predefined threshold ( p exc ) as a metric for the distance of a site to the niche edge. These metrics are unambiguously determinable, intuitive and evident. Stands at a central, a marginal and an intermediate niche position are taken to exemplify deduction and application of p occ and p exc . A regional exercise shows how a map of p exc may support forest management planning and decision making under severe uncertainty. A key advantage of our novel metrics is the reformulation of common species distribution model (SDM) outputs in terms of risk thereby accounting for two important sources of uncertainty. [ABSTRACT FROM AUTHOR]

• Extension of a stoichiometric model to account for climate change • Changing environment acts on the system through light-dependent carrying capacity • Stochastic modelling approach using intrinsic and extrinsic noise • Applied time to first transition to obtain latitudinal distribution of phytoplankton Internal and external factors on a producer-grazer system are important because these systems underpin many food chains and changes in them are therefore significant for many animal species. In particular, alterations between phytoplankton and zooplankton are important because they represent a vast source of nutriments for aquatic species. However, the impacts of different intrinsic and extrinsic noise on phytoplankton and zooplankton population dynamics are still missing. By applying stochastic methods to a popular stoichiometric model, the influence of internal and external factors on the system are explored, culminating with a proposition of how noise in the system can be used to examine the latitudinal spatial distribution of phytoplankton. This is achieved by expanding the stoichiometric model to account for macro environmental factors that induce noise in the system. First, the population dynamics under the influence of factors, such as predation, wind gusts and temperature, are individually documented in the paper. Thereafter, using the macro environmental factors such as climate change, stochastic simulations generate a spatial distribution in a latitudinal sense of phytoplankton where it is observed that the phytoplankton clusters are blooming at a higher than expected latitude. Therefore, the latitudinal distribution of phytoplankton of these clusters gives further evidence of the influence of arctic amplification. [ABSTRACT FROM AUTHOR]

Modeling the forest fire phenomena represents one of the most challenging and difficult problems, which need further investigations. Many research works have tackled this issue by developing simulation models in order to investigate the impact of climate change on fire spread behavior. In our previous work, a forest fire spread model has been introduced. The developed model takes into account some parameters that affect this phenomenon, such as topography, land use, nature and density of vegetation, humidity and wind by only considering uniform wind direction and speed. The work presented in this paper provides an improvement of this model for being integrated in real-sitting scenarios. In fact, a model of wind flow has been proposed. It is able to determine accurately the wind direction and speed according to the topography and land use. The proposed model was evaluated using dataset from a region in the north of Morocco, and results are reported to show the impact of the dynamic behavior of the wind on fire propagation. [ABSTRACT FROM AUTHOR]

The prospect of rapidly changing climates over the next century calls for methods to predict their effects on myriad, interactive ecosystem processes. Spatially explicit models that simulate ecosystem dynamics at fine (plant, stand) to coarse (regional, global) scales are indispensable tools for meeting this challenge under a variety of possible futures. A special class of these models, called landscape models (LMs), simulates dynamics at intermediate scales where many critical ecosystem processes interact. The complicated dependencies among climate, disturbance, and vegetation present a difficult challenge for LMs, however, because their simulation must reconcile processes and their interactions that occur at different spatial and temporal scales. In the absence of these interactions, key thresholds in ecosystem responses to changes in climate may go undetected or misrepresented. In this paper, we present a general strategy for constructing the next generation of LMs that ensures that interactions are modeled at appropriate scales of time and space, and that, when possible, processes representing these interactions are simulated mechanistically. We identify six key questions to frame this strategy and then provide guidance and possible solutions on the structure and content needed in future LMs to ensure that climate-vegetation-disturbance interactions are incorporated effectively. [ABSTRACT FROM AUTHOR]

The breeding success of many species depends on the alignment of the period of maximum demand of offspring with a period of food abundance. In this paper, we use piecewise-smooth differential equations to model the variation in the size of a population that has a short interval each year during which successful reproduction is possible. An increase in spring-time temperatures in recent decades has caused these intervals to advance, leading to temporal mismatches between species on different trophic levels. We first consider a one-species model which illustrates the dynamics of a population of specialist feeders over the course of a single breeding season and use it to examine how reproductive success depends on the population's distribution of breeding dates. We then introduce time-dependent switches to extend the model to a broader class of species. Steady-state solutions are used to measure the extent to which a migratory population can sustain itself over long time scales and repeated breeding events. The model's predictions agree with the observed negative effect on population size associated with an advance in the food season if not adequately compensated for by the population. Finally, we discuss how this model can be extended in order to encompass a wider range of ecological systems and to highlight the mechanisms over which climate change may exert an influence. [ABSTRACT FROM AUTHOR]

In a recent issue of this journal, Loehle (2014) presents a “minimal model” for estimating climate sensitivity, identical to that previously published by Loehle and Scafetta (2011). The novelty in the more recent paper lies in the straightforward calculation of an estimate of transient climate response based on the model and an estimate of equilibrium climate sensitivity derived therefrom, via a flawed methodology. We demonstrate that the Loehle and Scafetta model systematically underestimates the transient climate response, due to a number of unsupportable assumptions regarding the climate system. Once the flaws in Loehle and Scafetta's model are addressed, the estimates of transient climate response and equilibrium climate sensitivity derived from the model are entirely consistent with those obtained from general circulation models, and indeed exclude the possibility of low climate sensitivity, directly contradicting the principal conclusion drawn by Loehle. Further, we present an even more parsimonious model for estimating climate sensitivity. Our model is based on observed changes in radiative forcings, and is therefore constrained by physics, unlike the Loehle model, which is little more than a curve-fitting exercise. [ABSTRACT FROM AUTHOR]

• LUE-VPM produced better quality NPP than MODIS products. • PAR produced from high-resolution DEM can be used in regional NPP modelling. • The MODIS NPP is statistical different with the in-situ measurements in Zeku implementation. Accurate estimation of grassland Net Primary Productivity (NPP) is one of the most important steps in monitoring and further analysing the status of the grassland ecosystem. However, the quality of regional or even global grassland NPP estimations are commonly limited by the two most important input datasets: Photosynthetic Active Radiation (PAR) and climatic data, which make the NPP estimate unrepresentative or even inaccurate. This paper uses a Light Use Efficiency with Vegetation Photosynthesis Model (LUE-VPM) to estimate the high-quality grassland NPP in Zeku. By using the PAR data derived from high resolution DEM products and the improved land surface temperature data, the NPP produced by LUE-VPM shows that it is statistically the same as in situ converted NPP with a p-value of t -test equalling 0.90 (the RMSE between the two is 97.77 gC/m2), while MODIS NPP in Zeku is statically different from the all the in-situ sampling results with a p-value of t-text smaller than 0.03. The results of this paper provided a better estimate than MODIS NPP, and LUE-VPM can be used for the high-quality regional grassland NPP estimation when the quality of PAR data and climatic data improved. [ABSTRACT FROM AUTHOR]

Abstract: Wetlands are considered fundamental for the conservation of many species of birds. They are among the most threatened habitats on Earth, due to area loss through drainage and other land use changes, which lead to large-scale redistributions of birds and population declines. Although anthropogenic activities in wetlands tend to negatively affect water birds, by changing their natural habitat conditions, many species may benefit from the creation of artificial reservoirs, using these areas to feed, breed and rest. Reservoirs and some types of flooded agricultural fields are preferred by many species using Mediterranean extents for wintering and as stopover migration spots. The present paper examined the applicability of a Stochastic Dynamic Methodology (StDM) to predict the dynamics of the daylight water bird assemblages on artificial small reservoirs facing different climatic and hydrological conditions. The final model provided some basis to analyze the responses at community level (total abundance, species richness and behavioural categories) under very complex and variable environmental scenarios. The obtained results show how expected changes in the climatic and hydrological patterns will alter the bird community daylight dynamics, namely their composition and behavioural interactions. [Copyright &y& Elsevier]

Abstract: Assessments of future tree species’ distributions should account for time lags in the adaptation of their external range limits to climatic changes. In simulation experiments it is therefore necessary to capture processes that influence such time lags, in particular tree species’ migration. We hypothesise that directional processes such as migration are sensitive to the exact sequence of simulated climate influences, and that the uncertainty associated with a given interannual climate variability has to be accounted for when simulating migration explicitly. In this paper we used the intermediate-complexity multi-species model TreeMig to examine whether different realisations of future climate influences with the same temporal mean and the same interannual variability cause fundamental differences in simulated migration. We assume that the impact of interannual climate variability becomes most apparent in situations which critically influence regeneration and survival. Such situations arise, for example, when species’ sensitivities to climate, competition and spatial fragmentation interact. We therefore developed an illustrative and realistic simulation setup representing this situation. We simulated the northwards migration of the sub-Mediterranean tree species Ostrya carpinifolia Scop. (European Hop Hornbeam) through the highly fragmented and climatically heterogeneous landscape of the Swiss Alps. Situations critically influencing regeneration and survival can lead to low species’ abundances. Before investigating effects of interannual climate variability, we therefore tested whether the continuous representation of species’ cell populations in TreeMig, which allows for infinitesimal population densities, can have side effects on simulated migration. Specifically, we tested for effects of minimum density thresholds, i.e. thresholds below which a species is treated as absent. We found that small thresholds in the magnitude of one individual per km2 cell have a considerable impact on simulated migration, and can even impede migration in situations critical for regeneration and survival. To test for effects of interannual climate variability, we compared simulation results from multiple repetitions driven by different annual climate time series generated stochastically from the same probability distribution. Results from these repetitions were additionally compared to results from simulations driven by cyclically repeated climate and steadily applied mean climate, respectively. These comparisons were conducted for different species parameter sets within the plausible parameter range of O. carpinifolia to account for potential interactions between species’ sensitivities and the environment. Simulated tree species’ migration was highly dependent on the species parameters applied and markedly influenced by interannual climate variability. Notable divergence in species’ spread resulted amongst multiple realisations of annual climate time series stochastically sampled from the same probability distribution. We conclude that uncertainty associated with interannual climate variability has to be accounted for. Single realisations can be insufficient and mean value simulations as well as averages of output results can be too simplistic to reflect possible outcomes of tree species’ migration. [Copyright &y& Elsevier]

Abstract: Many organisms are threatened by global climate change, but as sessile organisms, plants are particularly vulnerable. Unable to individually move to areas with more suitable conditions as the climate changes, plants must instead rely on their seeds dispersing far and often in order to migrate fast enough to keep pace with the changing climate. The fragmentation of natural landscapes by clearing for agricultural or urban development, or a species’ requirement for particular soil types or topography, makes this migration even more problematic. The likelihood of a plant species persisting will thus depend on factors such as the rate of climate change, the degree of landscape fragmentation, and also the plant species’ lifecycle, seed production, dispersal, and establishment. This paper presents a dynamic model, called PPunCC (Plant Persistence under Climate Change), that represents the important factors and processes likely to affect a plant species’ capacity to migrate across a landscape fast enough to avoid extinction. We use the model to show that creating large-scale corridors that increase the connectivity of fragmented landscapes through targeted restoration could help species migrate naturally and find suitable environments in new climates. We also examine the effect of varying the key point of vulnerability within its lifecycle between juvenile establishment, adult mortality or seed production and conclude that this has a relatively minor effect on the prognosis for the plant and does not affect the predicted benefits of restoration. [Copyright &y& Elsevier]

Conservation and land management organizations such as The Nature Conservancy are developing strategies to distribute conservation efforts over larger areas. Relative to fee-simple protection efforts, strategies that allow ecologically sustainable timber harvest and recreation activities, such as working forest conservation easements, should yield greater socioeconomic benefits (ecosystem services) with less investment per area without significantly compromising the conservation of biodiversity (ecological targets). At the same time, climate change may profoundly influence forest resilience to management strategies in the coming century. As a result, there are many possible scenarios for the future of our forests and significant uncertainty for practitioners and decision makers. Yet, monitoring efforts aimed at evaluating the effectiveness of conservation strategies span decades or longer, leading to a lag in knowledge transfer and delayed adaptive management. To explore potential outcomes for biodiversity, provisioning of ecosystem services, and resilience of our forests resulting from various management strategies and climate change projections, we developed an approach that integrates quantitative, spatially explicit landscape modeling with scenario-building informed by expert knowledge. In this paper, we present our experiences applying this approach to two conservation project areas in the western Great Lakes region of the U.S. For each project area, spatially explicit landscape simulations were performed using the VDDT©/TELSA© software suite (ESSA Technologies, Ltd.). At key points in the process, we infused the modeling efforts with expert knowledge via interactive in-person or web-based workshops and an online collaborative tool. Here, we capture our experiences applying the scenario building and modeling approach to forests in the western Great Lakes region and our efforts to make the process transparent and responsive to local and regional experts. It is our intent that this approach be transferable and implemented in future landscape scale conservation projects. [Copyright &y& Elsevier]

Given the increasing expansion of human dominated landscapes it often becomes necessary to relocate endangered and at-risk species from existing habitat areas. Further, there is growing research stating that climatic and atmospheric changes attributed to climate change are already affecting species distributions and geographic ranges, requiring endangered species to be relocated. Existing reserve design models mostly focus on choosing the optimal land area given existing species distributions and have not incorporated species relocation as a criterion. This paper introduces linear integer programming formulations for the relocation of multiple populations of a species at risk to clustered conservation areas. We present a basic clustered relocation model and extend the model to minimize the distances of relocation. We apply the models to a dataset related to Gopher Tortoise (GT), a keystone species currently considered ‘at risk’, at Ft. Benning Georgia where expanding military training needs require the relocation of GTs. We present the results and discuss the trade-off between compactness and relocation considerations using an efficiency frontier. [Copyright &y& Elsevier]

In this paper is presented a methodological approach which integrates statistic modelling and 2-D cellular automata (CA) in order to describe tree species shifts responding to the climate changes foreseen for Italy in the 21st century. Five Italian tree species populations of Abies alba, Pinus sylvestris, Fagus sylvatica, Acer campestris and Quercus suber and their actual potential distributions (PDs) – represented by Importance Value (IV), have been considered. Environmental and climatic relationships have been modelled through application of a new statistical methodology called extreme discretization, where the PD of a species was considered as a random field. The IV-based PD has been spatialized through a probability function π(A,S), which represented the spatio-temporal relationships between IV values and climatic (A) and geo-morphological (S) variables. For each tree species π =(A,S) has been estimated and inserted as rule in the 2-D cellular automata. The latter, acting by a Moore neighbouring, took in consideration also the suitability map for tree species, which has been obtained by land cover map. Two time frames (2050 and 2080) and two climatic scenarios (A2 and B1) have been considered. Results described a general reduction of the IV values and their distribution for A. alba, P. sylvestris and F. sylvatica, in both climatic scenarios, whereas an increase of IVs and distribution for Q. suber and only a slight increment of distribution for A. campestris was mainly observed under the B1 scenario, but not for the more limiting A2 scenario. Convergent results have been obtained with respect to other simulation systems concerning the shift of tree species responding to different climatic change scenarios but lacking of the description of dynamical paths. Our approach seems natural and practical to describe such phenomena. The transition rules for the CA and the parameters taken into account for the construction of the probabilistic models can be surely improved to obtain a more realistic pattern of tree species shifts. Future efforts should be made to take in account the inter-specific relationships inside the Italian forest ecosystems, in order to also consider the competiveness for resources that exert some effects on the plant distribution both in time and space. [Copyright &y& Elsevier]

Restoration of abandoned and degraded ecosystems through enhanced management of mature remnant patches and naturally regenerating (regrowth) forests is currently being used in the recovery of ecosystems for biodiversity protection and carbon sequestration. Knowledge of long-term dynamics of these ecosystems is often very limited. Vegetation models that examine long-term forest growth and succession of uneven aged, mixed-species forest ecosystems are integral to the planning and assessment of the recovery process of biodiversity values and biomass accumulation. This paper examined the use of the Ecosystem Dynamics Simulator (EDS) in projecting growth dynamics of mature remnant brigalow forest communities and recovery process of regrowth brigalow thickets. We used data from 188 long-term monitored plots of remnant and regrowth forests measured between 1963 and 2010. In this study the model was parameterised for 34 tree and shrub species and tested with independent long-term measurements. The model closely approximated actual development trajectories of mature forests and regrowth thickets but some inaccuracies in estimating regeneration through asexual reproduction and mortality were noted as reflected in stem density projections of remnant plots that had a mean of absolute relative bias of 46.2 (±12.4)%. Changes in species composition in remnant forests were projected with a 10% error. Basal area values observed in all remnant plots ranged from 6 to 29m2 ha−1 and EDS projections between 1966 and 2005 (39 years) were 68.2 (±10.9)% of the observed basal area. Projected live aboveground biomass of remnant plots had a mean of 93.5 (±5.9)tha−1 compared to a mean of 91.3 (±8.0)tha−1 observed in the plots. In regrowth thicket, the model produced satisfactory projections of tree density (91%), basal area (89%), height (87%) and aboveground biomass (84%) compared to the observed attributes. Basal area and biomass accumulation in 45-year-old regrowth plots was approximately similar to that in remnant forests but recovery of woody understorey was very slow. The model projected that it would take 95 years for the regrowth to thin down to similar densities observed in original or remnant brigalow forests. These results indicated that EDS can produce relatively accurate projections of growth dynamics of brigalow regrowth forests necessary for informing restoration planning and projecting biomass accumulation. [Copyright &y& Elsevier]

Climate change is affecting biodiversity worldwide inducing species to either “move, adapt or die”. In this paper we propose a conceptual framework for analysing range shifts, namely a catalogue of the possible patterns of change in the distribution of a species along elevational or other environmental gradients and an improved quantitative methodology to identify and objectively describe these patterns. Patterns are defined in terms of changes occurring at the leading, trailing or both edges of the distribution: (a) leading edge expansion, (b) trailing edge retraction, (c) range expansion, (d) optimum shift, (e) expansion, (f) retraction, and (g) shift. The methodology is based on the modelling of species distributions along a gradient using generalized additive models (GAMs). Separate models are calibrated for two distinct periods of assessment and response curves are compared over five reference points. Changes occurred at these points are formalized into a code that ultimately designates the corresponding change pattern. We tested the proposed methodology using data from the Swiss national common breeding bird survey. The elevational distributions of 95 bird species were modelled for the periods 1999–2002 and 2004–2007 and significant upward shifts (all patterns confounded) were identified for 35% of the species. Over the same period, an increase in mean temperature was registered for Switzerland. In consideration of the short period covered by the case study, assessed change patterns are considered to correspond to intermediate patterns in an ongoing shifting process. However, similar patterns can be determined by habitat barriers, land use/land cover changes, competition with concurrent or invasive species or different warming rates at different elevations. [Copyright &y& Elsevier]

Long-term future development of European agriculture within the global market is highly uncertain, but can potentially have large impacts on the future of agricultural businesses, rural communities and amenities such as traditional landscapes and biodiversity. Despite great uncertainties it is of interest to explore the extent of these potential changes. This paper provides an explorative scenario of the European crop production in a liberalised world without European Union (EU) market interventions. The results do not form a prediction or a business as usual scenario, but rather a plausible and salient thought-experiment of a possible future based on the consistent integration of current conceptual and quantitative models. Future scenarios for climate, demography, technology and global demand for agricultural commodities are used to assess the competitiveness of European agriculture. Regional economic competitiveness is determined by combining indicators for the economic strength of farms in a region and population pressure on agricultural land, and subsequently used to determine where agricultural production is likely to sustain under the market liberalisation scenario. The method is illustrated for the 27 EU member state countries for three commodities: wheat, potato and milk (relying on grass). Results include maps of the dominant wheat, potato and milk producing regions across Europe as projected for 2050. They show that due to increased agricultural productivity, less agricultural land will be needed to supply the European demand for food and feed. In addition, production will concentrate in those regions which have a comparative advantage. This potentially leads to a strong polarisation between north-western Europe and southern Europe, which faces negative impacts of climate change and central and northern Europe where agricultural businesses lag in economic strength and farm size. A contrasting policy intervention scenario illustrates how differences in ... [Copyright &y& Elsevier]

Abstract: The effects of habitat fragmentation on species may change seasonally mainly due to variations in resource availability and biotic interactions. In critical periods, such as winter, when the importance of intraspecific competition diminish, species may relax their environmental requirements widening their ecological niche to exploit the scarcer trophic resources more efficiently in comparison with spring. Those variations in niche width may implicate seasonal expansions/retractions in species distribution. In this sense, an integrated knowledge on the spatial arrangement of breeding and wintering suitable patches is essential to infer seasonal movements (migratory connectivity). This paper shows that little bustard environmental preferences were more predictable and complex (controlled by a larger number of environmental factors) in spring than in winter, when potential distribution and ecological niche width were slightly larger. In spring, habitat variables (i.e. percentage of dry crops and pasturelands and altitude) ruled species’ distribution; while, winter pattern was driven by mixed criteria, based on both habitat and climate (i.e. percentage of dry crops and wastelands and winter rainfall). Suitable patches were more connected across spatial scales in winter than in spring, i.e. landscape was perceived as less fragmented. The overlap between potential breeding and wintering distribution areas was high. In fact, most of the predicted wintering areas coincided or showed high connectedness with predicted breeding patches. Conversely, there were significant breeding patches that were predicted with low suitability, showing little connectedness with potential winter areas. Spring habitat was a better predictor of little bustard’s wintering range than vice versa, which has clear management implications (preserving breeding sites closer to wintering areas ensures the conservation of a larger proportion of the total distribution range). This is an example of how predictive large-scale modeling procedures can contribute to the optimization of land management aimed at species conservation. [Copyright &y& Elsevier]

• The positional TOPSIS, combined with different optimal tail selection methods of the variable distribution, is proposed to assess the sustainable development of territorial units. • The cluster compactness measure is proposed to evaluate the classification results of the positional TOPSIS with the optimal tail selection of variable distribution. • The automated Eye-Ball method and the Guillou and Hall's method enable correct identification of extreme values of variables describing the economic, social, and environmental development of municipalities. • The positional TOPSIS with selected optimal tail selection methods enable correct classification of the municipalities and assessment of their sustainable development in terms of levels and statuses. In the face of accelerating climate change, economic instability and limited resources, it is essential to find indicators that better measure the sustainable development of territorial units. Despite ambiguous definitions of sustainable development, numerous studies describe quantitative indicators of sustainable development. Main methodological problems include data availability and their use, spatial and time scales, selection of indicators, and their aggregation. Usually, analysts and decision-makers apply well known conventional methods that have numerous limitations. These methods in many cases cause excessive simplification of the real phenomena and relations between their determinants. Moreover, variables describing the development of territorial units (especially the environmental pillar) are often characterized by strong asymmetry or extreme observations. Even a single outlier for a given territorial unit may significantly affect the analysis and the resulting conclusions. The objective of this paper is to present the application potential of a Multi-Criteria Decision Making (MCDM) approach with an optimal tail selection to assess the sustainable development of territorial units. The proposed approach is based on the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and different methods of optimal tail selection of variable distribution. The Oja's spatial median and the optimal tail selection methods are used to reduce the impact of strong asymmetry and extreme values of variables. Moreover, the measure of cluster compactness is proposed for the evaluation of the proposed method. The proposed approach was used to assess the sustainable development of municipalities in Poland in 2018. We found that the automated Eye-Ball method and the Guillou and Hall's method enabled correct identification of extreme values of variables describing the economic, social, and environmental development of municipalities. This made it possible to distinguish the types of sustainable development level and its pillars and enabled to provide the statuses of the sustainable development level of the examined municipalities with respect to other municipalities in Poland. [Display omitted] [ABSTRACT FROM AUTHOR]

Abstract: The impact of climate changes on the pollution levels in Denmark is the major topic of this paper. Variations of the Danish air pollution levels that are caused by climatic changes are studied together with variations caused by other factors (emissions, inter-annual variability of meteorological conditions, etc.). The Unified Danish Eulerian Model (UNI-DEM) was run on a fine, 10km×10km, grid over a space domain covering all of Europe to minimize the influence of the boundary conditions on the Danish pollution levels. This study is based on four categories of scenarios: (i) traditional scenarios, (ii) climatic scenarios, (iii) scenarios with variations of the human-made (anthropogenic) emissions and (iv) scenarios in which the biogenic emissions were varied. The total number of applied scenarios was 14, and a time-period of 16 years was used. The results show clearly that although the concentrations of the major pollutants do not depend too much on the climatic changes, some quantities, in particular quantities related to high ozone levels, might be increased significantly as a result of the warming trends in the future climate. The reason for this phenomenon is explained. [Copyright &y& Elsevier]

Abstract: Industrialized countries agreed on a reduction of greenhouse gas emissions under the Kyoto Protocol. Many countries elected forest management activities and the resulting net balance of carbon emissions and removals of non-CO2 greenhouse gases by forest management in their climate change mitigation measures. In this paper a generic dynamic forestry model (FORMICA) is presented. It has an empirical basis. Several modules trace C pools relevant for the Kyoto Protocol and beyond: biomass, litter, deadwood and soil, and harvested wood products. The model also accounts for the substitution of fossil fuels by wood products and bioenergy. FORMICA was used to first study the model sensitivity and uncertainty based on data from Thuringia, a federal state of Germany, to determine the major sources of uncertainty in carbon accounting at different levels of carbon pool aggregation (biomass, ecosystem, forestry sector and enhanced forestry sector including the accumulated substitution effect). Rotation length and maximum increment contributed most to uncertainty in biomass. The influence of the latter did not diminish with higher level of pool aggregation. Uncertainty in the enhanced forestry sector was to a smaller degree controlled by product and substitution related parameters. Relative uncertainty decreased with the level of aggregation and comprehensiveness of the carbon budget. In a second step the model estimated the sink potential of the Thuringian forestry sector. The projected average biomass sink for the period of 2003–2043 of 0.6tCha−1 year−1 could be increased by 50% by broadening the perspective to the entire forestry sector, including substitution effects. A simulation of forest conservation on 20% of the forest area increased C fixation. However, even in the biomass C pool the expected C stock changes did not exceed the estimated uncertainty of 40%. A higher level of aggregation (i.e. the inclusion of soil and litter, product pool and substitution effects) decreases relative uncertainty but also diminishes differences between different management options. The analysis demonstrates that the choice of management mitigation options under an accounting scheme should include the impacts on forest products and of substitution effects. [Copyright &y& Elsevier]

Antarctic lakes with simple plankton ecosystems are believed to be sensitive biological indicators of climate change. Models of the physical environment, in particular the ice layer, support understanding of how the ecosystems respond to meteorological variables. This paper describes how data from a previously reported automatic measuring probe and meteorological data from Davis station were used to develop a detailed thermodynamic model of the ice layer on Crooked Lake, one of the largest and deepest freshwater lakes in Antarctica. The general model structure is similar to a previously reported model of sea ice but with modifications specific to the Antarctic freshwater lake case informed by the data. The model inputs are atmospheric variables as well as water temperature, ice albedo and the radiation extinction coefficient for the ice. Heat and radiation fluxes at the ice–air and ice–water boundaries are calculated using equations chosen for their suitability for the Antarctic. In the case of shortwave radiation, equations were fitted to data from the automatic probe. Using the heat fluxes to establish boundary conditions, and incorporating the known thermodynamic properties of ice, the temperature profile within the ice and the resulting growth and melt of the ice can be calculated. The model uses a largely mechanistic approach, with most equations taken from established thermodynamic theories or empirical studies and only one adjustable parameter related to the sensible heat flux from the water, which is not easily calculated from the available data. It was found to accurately reproduce ice temperature and ice thickness data for the year 2003, with r 2 =0.89, n =2005. Finally, the model was simplified to run with air temperature as the only input variable and was shown to perform well—this suggests that freshwater lake ice is affected more by air temperature than any other variable, and is therefore a useful indicator of climate change in its own right. [Copyright &y& Elsevier]

Abstract: In this paper, control theory is used to study the connection between human activities and global warming. A feedback model is proposed and tested against temperature and carbon dioxide concentration historical data. Four scenarios are taken into account and simulated by the model; stability analysis is also discussed. The model proposed here simulates the historical data correctly and the scenarios show that, even in the case of dramatic reduction of the anthropogenic carbon dioxide emission, the temperature will not decrease for a certain time. Stability analysis reveals a complex pole near the unit circle. This means that, although the system at the moment is stable, it is very close to becoming unstable with unpredictable consequences on climate change. [Copyright &y& Elsevier]

The PICES CCCC (North Pacific Marine Science Organization, Climate Change and Carrying Capacity program) MODEL Task Team achieved a consensus on the structure of a prototype lower trophic level ecosystem model for the North Pacific Ocean, and named it the North Pacific Ecosystem Model for Understanding Regional Oceanography, “NEMURO”. Through an extensive dialog between modelers, plankton biologists and oceanographers, an extensive review was conducted to define NEMURO''s process equations and their parameter values for distinct geographic regions. We present in this paper the formulation, structure and governing equations of NEMURO as well as examples to illustrate its behavior. NEMURO has eleven state variables: nitrate, ammonium, small and large phytoplankton biomass, small, large and predatory zooplankton biomass, particulate and dissolved organic nitrogen, particulate silica, and silicic acid concentration. Several applications reported in this issue of Ecological Modelling have successfully used NEMURO, and an extension that includes fish as an additional state variable. Applications include studies of the biogeochemistry of the North Pacific, and variations of its ecosystem''s lower trophic levels and two target fish species at regional and basin-scale levels, and on time scales from seasonal to interdecadal. [Copyright &y& Elsevier]

Abstract: Given the importance of knowledge of species distribution for conservation and climate change management, continuous and progressive evaluation of the statistical models predicting species distributions is necessary. Current models are evaluated in terms of ecological theory used, the data model accepted and the statistical methods applied. Focus is restricted to Generalised Linear Models (GLM) and Generalised Additive Models (GAM). Certain currently unused regression methods are reviewed for their possible application to species modelling. A review of recent papers suggests that ecological theory is rarely explicitly considered. Current theory and results support species responses to environmental variables to be unimodal and often skewed though process-based theory is often lacking. Many studies fail to test for unimodal or skewed responses and straight-line relationships are often fitted without justification. Data resolution (size of sampling unit) determines the nature of the environmental niche models that can be fitted. A synthesis of differing ecophysiological ideas and the use of biophysical processes models could improve the selection of predictor variables. A better conceptual framework is needed for selecting variables. Comparison of statistical methods is difficult. Predictive success is insufficient and a test of ecological realism is also needed. Evaluation of methods needs artificial data, as there is no knowledge about the true relationships between variables for field data. However, use of artificial data is limited by lack of comprehensive theory. Three potentially new methods are reviewed. Quantile regression (QR) has potential and a strong theoretical justification in Liebig''s law of the minimum. Structural equation modelling (SEM) has an appealing conceptual framework for testing causality but has problems with curvilinear relationships. Geographically weighted regression (GWR) intended to examine spatial non-stationarity of ecological processes requires further evaluation before being used. Synthesis and applications: explicit theory needs to be incorporated into species response models used in conservation. For example, testing for unimodal skewed responses should be a routine procedure. Clear statements of the ecological theory used, the nature of the data model and sufficient details of the statistical method are needed for current models to be evaluated. New statistical methods need to be evaluated for compatibility with ecological theory before use in applied ecology. Some recent work with artificial data suggests the combination of ecological knowledge and statistical skill is more important than the precise statistical method used. The potential exists for a synthesis of current species modelling approaches based on their differing ecological insights not their methodology. [Copyright &y& Elsevier]

Abstract: A central issue in ecology is to determine how environmental variations associated with global climate change, especially changing temperatures, affect trophic interactions in various ecosystems. This paper develops a temperature-dependent, stage-based, discrete, cohort model of the population dynamics of an insect pest under pressure from a predator. Guided by experimental data, the model is applied specifically to predation of grasshoppers by rangeland lycosid spiders. The development rate of insect arthropods is strongly affected by temperature, and these temperature-dependent phenological effects couple with shifts in the daily activity periods for both prey and predator, thereby increasing or decreasing opportunities for interaction. The model addresses these effects quantitatively by introducing a temperature-dependent, joint-activity factor that enters the predator’s functional response. The model also includes a prey mortality rate that is temperature-dependent through the prey development rate. The model is parameterized using field and experimental data for spiders and grasshoppers. We investigate the effect of the solar power index (sunlight), mean temperature, and temperature variation, as measured by amplitude, on the developmental times and survivorship both with, and without, predation. We conclude that increasing variation in temperature results in a stronger relative effect on survivorship due to predation. [Copyright &y& Elsevier]

Abstract: The analysis of climatic and anthropogenic effects on mesoscale river basins has become one of the main concerns for hydrologists, environmentalists, and planners during the last decade. Many attempts at dealing with this issue have been proposed in the literature. In most studies, however, the main components of the system have been isolated in order to reduce the complexity of the system and its intrinsic uncertainty. In this paper, an attempt to couple two realms of the water system at a mesoscale catchment, namely, the hydrological behaviour of a catchment and the state of the land cover at a given point in time, is presented. Here, instead of using a standard hydrological model, various nonlinear models relating several runoff characteristics with physiographic, land cover, and meteorological factors were linked with a stochastic land use/cover change model. Then, using this integrated model, the magnitude of the effects of the hydrological consequences of land use/cover and climatic changes was assessed in a probabilistic way by a sequential Monte Carlo simulation provided four different scenarios which take into account likely developments of macroclimatic and socioeconomic conditions relevant for a given study area. The proposed methodology was tested in a river basin of approximately 120 km2 located to the south of Stuttgart, Germany. [Copyright &y& Elsevier]

Abstract: In semiarid savannas of Southern Africa current land use practices and climate change may lead to substantial changes of vegetation structure in the near future, however uncertainty remains about the potential consequences and the magnitude of change. In this paper we study the impact of climate change, cattle grazing, and wood cutting on shrub cover dynamics in savannas of the southern Kalahari. We use an established savanna ecosystem model to simulate landscape dynamics in terms of rainfall, fire and distribution of the dominant tree Acacia erioloba. We then incorporate these data into a spatial population model of the common, fleshy-fruited shrub Grewia flava and investigate shrub cover dynamics for a period of 100 years. Depending on the intensity of commercial wood cutting practices tree removal of A. erioloba led to a strong decline of the G. flava population, as shrub recruitment is concentrated in tree sub-canopies due to bird-mediated seed dispersal. Under climate change shrub cover slightly decreased with decreasing precipitation and was unchanged with increase in precipitation variability. Contrarily, grazing by cattle strongly increased shrub cover and facilitated shrub encroachment because of cattle-induced distribution of G. flava seeds into the matrix vegetation. Knowledge of the latter process is particularly important because shrub invasion is a major concern for conservation and savanna rangeland management as a result of its adverse effects on livestock carrying capacity and biodiversity. [Copyright &y& Elsevier]

Abstract: Uncertainties related to the climate impact studies are reviewed on the case of onion thrips in Slovenia. Paper illustrates cumulative uncertainty introduced by future emission scenarios, general circulation models, downscaling procedures, weather generators and impact (degree-day) models as common tools in climate impact studies. The result of cumulative uncertainty is a wide range of expected warming in Slovenia in the 21st century. An expected increase of air temperature, relative to 1990, is between 1.5 and 7°C till the end of present century. Due to heterogeneity of the present climate conditions in Slovenia, quite uniform air temperature increase on the entire area will have different impact on the change of cumulative degree-days (DD) and related number of generations of onion thrips per year (N gen) in different regions. An example of Ljubljana and Bilje shows that larger increase of DD and related N gen is expected for regions with warmer climate. As a result, more damage will probably be caused in areas that are already strongly exposed to the problem of onion thrips. Harmfulness of the pest in the areas, where it is not a serious threat at present, will also increase. However, we have to be fully aware of a large amount of uncertainty related to studies of climate change impact while interpreting their results. [Copyright &y& Elsevier]

Abstract: Climate variability at decadal scales influences not only the growth of widely distributed species such as Pinus ponderosa, but also can have an effect on the timing and severity of fire and insect outbreaks that may alter species distributions. In this paper, we present a spatial modelling technique to assess the influence of climatic variability on the annual productivity of P. ponderosa in the Pacific Northwest (PNW) of North America over the past 100 years and infer how a sustained change in climate might alter the geographic distribution of this species across defined ecotones. Field observations were used to establish criteria for P. ponderosa dominance including: (1) maximum summer leaf area index (LAI), ranges between 1.5 and 2.5; (2) 80% of available soil water is depleted during summer months; and (3) soil water will return to full capacity at least once during the year. Where these three criteria were not met, eventual replacement of P. ponderosa would be predicted. We utilized a simple physiological model, Physiological Principles for Predicting Growth (3-PG) to predict annual variation in LAI from climatic data provided by the Oregon Climate Service over the period from 1900 to 2000 and from broad scale 0.5°-spatial resolution future climate projections produced by the Hadley Climate Center, UK. From these simulations we produced a series of maps that display predicted shifts of zones where ponderosa pine might be expected to contract or expand its range if modeled climatic conditions at annual and decadal intervals were sustained. From the historical simulations, the most favorable year for pine dominance was 1958 and the least favorable, 1924. The most favorable decade was in the 1900s and the least favorable in the 1930s. The future predictions indicate a reduction in the current range of the P. ponderosa type along the western Cascade Range however, an increase along the east side and inland PNW. The model predicts that pine dominance should increase between 5 and 10% over the next century, mainly in inland Oregon, Idaho, and Washington. [Copyright &y& Elsevier]

The nitrogen and carbon dynamics of water-limited ecosystems are significantly controlled by the soil water content, which in turn depends on soil properties, climate, and vegetation characteristics. Because of its impact on soil aeration, microorganism environmental stress, and ion transport within the pore spaces, the soil water content controls the activity of microbial biomass with important effects on the rates of decomposition, mineralization, nitrification, and denitrification. Mineral nitrogen is mainly lost in the leaching and plant uptake processes, which are both controlled by the soil water content. To assess both the long-term and the short-term impact of soil moisture dynamics on the soil nitrogen and carbon budgets, models of the N and C cycles need to operate at daily resolutions (or higher). On the other hand, long-term projections require a stochastic modeling of the climate forcing to generate long replicates of the climate signal as well as to assess the system response to climate change. This paper reviews a modeling framework developed by the authors [Proc. R. Soc. Lond. A 455 (1999a) 3789; Adv. Water. Res. 26 (2003) 45; Adv. Water Resour. 26 (2003) 59; Sci. J. 5 (2003) 781] for the process-based analysis of soil moisture, nitrogen, and carbon dynamics, presenting a synthesis of the main results of those investigations. [Copyright &y& Elsevier]

This paper assesses potential changes in the distributions of 12 endemic rainforest vertebrates of the Wet Tropics of Northeastern Australia in response to global warming predictions. It is based on projecting models of current geographic distribution to modelled future conditions taken from regional climate models. In comparing vertebrate species with different mobility, adaptation potential and distribution, consequences of global warming for elements of the regional biodiversity were investigated. The bioclimatic program, BIOCLIM, was used to determine the current climatic ranges and the potential changes in the distributions of the 12 species under four climate change scenarios for the year 2100. Results suggest that even species with currently wide climatic ranges may become vulnerable. Species distribution area decreased by more than 50% on average. Furthermore, the remaining predicted range decreased from an average of 90% under a conservative scenario, to about 40% under a less conservative scenario. These results show how sensitively some species could react to climate change and emphasise the need for rapid action on global warming for biodiversity conservation. [Copyright &y& Elsevier]

This paper describes a process-based metapopulation dynamics and phenology model of prickly acacia, Acacia nilotica, an invasive alien species in Australia. The model, SPAnDX, describes the interactions between riparian and upland sub-populations of A. nilotica within livestock paddocks, including the effects of extrinsic factors such as temperature, soil moisture availability and atmospheric concentrations of carbon dioxide. The model includes the effects of management events such as changing the livestock species or stocking rate, applying fire, and herbicide application. The predicted population behaviour of A. nilotica was sensitive to climate. Using 35 years daily weather datasets for five representative sites spanning the range of conditions that A. nilotica is found in Australia, the model predicted biomass levels that closely accord with expected values at each site. SPAnDX can be used as a decision-support tool in integrated weed management, and to explore the sensitivity of cultural management practices to climate change throughout the range of A. nilotica. The cohort-based DYMEX modelling package used to build and run SPAnDX provided several advantages over more traditional population modelling approaches (e.g. an appropriate specific formalism (discrete time, cohort-based, process-oriented), user-friendly graphical environment, extensible library of reusable components, and useful and flexible input/output support framework). [Copyright &y& Elsevier]

The potential effects of climate change on vegetation are of increasing concern. In the Mediterranean region, the dominant impact of climate change is expected to be through the modification of water balance. In this paper we present a model developed to predict drought-induced mortality of woody plants under different climatic scenarios. The model is physiologically-based and simulates water transport within individual woody plants, which can be isolated or competing for a common water resource. The model assumes that plant mortality is controlled by the carbon balance: when the plant is unable to transport water to the leaves it ceases to acquire carbon and, if this situation lasts long enough, it can no longer survive. In the particular application that we report in this study, two evergreen species are compared, Quercus ilex and Phillyrea latifolia, which were very differently affected by the acute drought that occurred in E Spain in summer 1994. While in some Q. ilex populations the amount of individuals that dried completely was up to 80%, P. latifolia showed almost no damage. During the years 1999 and 2000, canopy transpiration was monitored using sap-flow sensors in individuals of these two species in a Holm-oak forest from NE Spain. A Generalised Likelihood Uncertainty Estimation (GLUE) approach was used to calibrate the model against sap-flow measurements. The only difference between species that was introduced ‘a priori’ was that Q. ilex was more vulnerable to xylem embolism than P. latifolia (based on our own measurements in the study area). During the calibration process the information provided by the measured sap flows was used to retain the more likely parameter sets for each species. These parameter sets were used in all the following simulations. The model was able to accurately simulate transpiration dynamics of the two species in the study area. When the meteorological conditions of summer 1994 were introduced, the model outputs also reproduced the differential impact that drought had on the two species studied. In the simulations under climate change two factors were explored: the increase in mean temperature (+1.5, +3 and +4.5 °C) through its effect on ET, and the duration of summer drought. Under any of the scenarios, mortalities were much higher for Q. ilex: while this species was predicted to survive with less than 5% mortality droughts of up to 84–94 days, the mortality of P. latifolia reached 5% between days 133 and 150. For droughts longer than 3 months, which is approximately the current drought duration in the study area for dry years, the mortality of Q. ilex increased sharply. These results are discussed in relation to the possible long-term impacts of climate change on Q. ilex-dominated forests. [Copyright &y& Elsevier]

To predict the potential effects of future global environmental changes (e.g. climate, land-use, fire disturbance, and forest harvesting) on the sustainability of forest ecosystems, forest resource managers will need forest simulation models. Basic approaches to modelling forest growth and dynamics include the use of empirical, mechanistic, and hybrid forest simulation models. In this paper, a hybrid, monthly time-step model of forest growth and carbon dynamics (TRIPLEX) is described and tested. The TRIPLEX model integrates the forest production model of 3-PG (For. Ecol. Manage. 95 (1997) 209), the forest growth and yield model of TREENYD3 (Ecol. Model. 90 (1996) 187), and the soil–carbon–nitrogen model of CENTURY4.0 (Global Biogeochem. Cycles 7 (1993) 785). The model is intended to be comprehensive without becoming complex, and minimizes the number of input parameters required, while capturing key processes and important interactions between the carbon and nitrogen cycles of forest ecosystems. It is designed as a hybrid of both empirical and mechanistic components that can be used for (1) making forest management decisions (e.g. growth and yield prediction), (2) quantifying forest carbon budgets, and (3) assessing the effects of climate change in both the short and long term. We tested TRIPLEX against age-dependent growth measurements from 12 permanent sample plots (PSP) in jack pine (Pinus banskiana Lamb.) stands in northern Ontario (Canada). Comparisons of simulated stand growth variables (e.g. tree diameter, height, and stem density) with those observed in PSPs indicated a good agreement over 30 years. Predictions of tree total volume and aboveground biomass were within the expected range for these plots. While the model is promising, future modifications are discussed. [Copyright &y& Elsevier]

Predicting impacts of climate change or alternative management on both food production and environment safety in agroecosystems is drawing great attention in the scientific community. Most of the existing agroecosystem models emphasize either crop growth or soil processes. This paper reports the latest development of an agroecosystem model (Crop-DNDC) by integrating detailed crop growth algorithms with an existing soil biogeochemical model, DNDC (Li et al., J. Geophys. Res. (1992) 9759). In the Crop-DNDC model, crop growth is simulated not only by tracking crop physiological processes (phenology, leaf area index, photosynthesis, respiration, assimilate allocation, rooting processes and nitrogen uptake), but also by calculating water stress and nitrogen stress, which were closely related to soil biogeochemical processes and hydraulic dynamics. Crop-DNDC also quantifies crop residue incorporated in the soil at the end of each growing season. Thus the model has tightly coupled crop growth algorithms with soil biogeochemical components, and simulates carbon, nitrogen and water cycles in agroecosystems with a relatively complete scope. The model was validated against field measurements, including soil moisture, leaf area index, crop biomass and nitrogen content, and the modeled results were in agreement with observations on soil carbon dynamics and trace gas emissions as well. Sensitivity tests demonstrated that the modeled results in crop yield, soil carbon dynamics and trace gas emissions were sensitive to climate conditions, atmospheric CO2 concentration and various farming practices. There are potentials of applying the model for simultaneously predicting effects of changes in climate or management on crop yield, soil carbon sequestration and trace gas emissions. [Copyright &y& Elsevier]