Large-scale model-based assessment of deer-vehicle collision risk

Ungulates, in particular the Central European roe deer Capreolus capreolus and the North American white-tailed deer Odocoileus virginianus, are economically and ecologically important. The two species are risk factors for deer-vehicle collisions and as browsers of palatable trees have implications for forest regeneration. However, no large-scale management systems for ungulates have been implemented, mainly because of the high efforts and costs associated with attempts to estimate population sizes of free-living ungulates living in a complex landscape. Attempts to directly estimate population sizes of deer are problematic owing to poor data quality and lack of spatial representation on larger scales. We used data on >74,000 deer-vehicle collisions observed in 2006 and 2009 in Bavaria, Germany, to model the local risk of deer-vehicle collisions and to investigate the relationship between deer-vehicle collisions and both environmental conditions and browsing intensities. An innovative modelling approach for the number of deer-vehicle collisions, which allows nonlinear environment-deer relationships and assessment of spatial heterogeneity, was the basis for estimating the local risk of collisions for specific road types on the scale of Bavarian municipalities. Based on this risk model, we propose a new "deer-vehicle collision index" for deer management. We show that the risk of deer-vehicle collisions is positively correlated to browsing intensity and to harvest numbers. Overall, our results demonstrate that the number of deer-vehicle collisions can be predicted with high precision on the scale of municipalities. In the densely populated and intensively used landscapes of Central Europe and North America, a model-based risk assessment for deer-vehicle collisions provides a cost-efficient instrument for deer management on the landscape scale. The measures derived from our model provide valuable information for planning road protection and defining hunting quota. Open-source software implementing the model can be used to transfer our modelling approach to wildlife-vehicle collisions elsewhere.     

Design of forest management planning DSS for wildfire risk reduction

Forest management planning is generally a complicated task. The amount of data, information and knowledge involved in the management process is often overwhelming. Decision support systems can help forest managers make well documented decisions concerning forest management planning. These systems include a wide variety of components, depending on the management goals of the forested land. Although an increased growth of decision support systems in specific domains of forest management planning exists, there is no special design model for the deployment of forest management planning. To this direction, this paper has the following objectives: Firstly, to propose a conceptual design model for developing goal-driven forest management planning decision support systems. Secondly, to apply the design model for a particular case of these systems, the wildfire risk reduction decision support systems. Thirdly, to present the deployment of a wildfire risk reduction decision support system as well as its results for a specific forest area.     

Exploring trade-offs in agro-ecological landscapes: Using a multi-objective land-use allocation model to support agroforestry research

Finding the optimal land allocation for providing ecosystem services, conserving biodiversity and maintaining rural livelihoods is a key challenge of agricultural management and land-use planning. Agroforestry has been widely discussed as a sustainable land-use solution and as one strategy to improve the provision of multiple ecological and economic functions in agricultural landscapes. In this study, we use the backdrop of agroforestry research to evaluate a method from the multi-criteria decision analysis toolbox: robust multi-objective optimization. The key feature of this modelling approach is its capacity to integrate uncertain ecological and socio-economic data. We illustrate the optimization model with a case study from eastern Panama, showing how the model can bring together scientific and practical knowledge to provide potentially desirable landscape compositions from the perspective of farmers, a public perspective, and a compromise solution. Example results of our case study show how to assess whether agroforestry is a desirable component in a landscape composition to satisfy multiple objectives of different interest groups. Furthermore, we use the model to demonstrate how different objectives influence the optimal area share and type of agroforestry. Due to its parsimonious nature, the model could be used as a starting point of an interactive co-learning process with decision-makers, researchers and other stakeholders. The model, however, is not yet suitable for an exact prediction of future land-use dynamics, for questions of spatially explicit land-use configuration, studies going beyond the regional scale or for socio-economic interactions of agents. Therefore, we outline future research needs and recommendations for other types of models or hybrid approaches.     

Extended statistical approaches to modelling spatial pattern in biodiversity in northeast New South Wales. II. Community-level modelling

Regional conservation planning can often make more effective use of sparse biological data by linking these data to remotely mapped environmental variables through statistical modelling. While modelling distributions of individual species is the best known and most widely used approach to such modelling, there are many situations in which more information can be extracted from available data by supplementing, or replacing, species-level modelling with modelling of communities or assemblages. This paper provides an overview of approaches to community-level modelling employed in a series of major land-use planning processes in the northeast New South Wales region of Australia, and evaluates how well communities and assemblages derived using these techniques function as surrogates in regional conservation planning. We also outline three new directions that may enhance the effectiveness of community-level modelling by: (1) more closely integrating modelling with traditional ecological mapping (e.g. vegetation mapping); (2) more tightly linking numerical classification and spatial modelling through application of canonical classification techniques; and (3) enhancing the applicability of modelling to data-poor regions through employment of a new technique for modelling spatial pattern in compositional dissimilarity.     

Supporting Fisheries Management by Means of Complex Models: Can We Point out Isles of Robustness in a Sea of Uncertainty?

Ecosystems are usually complex, nonlinear and strongly influenced by poorly known environmental variables. Among these systems, marine ecosystems have high uncertainties: marine populations in general are known to exhibit large levels of natural variability and the intensity of fishing efforts can change rapidly. These uncertainties are a source of risks that threaten the sustainability of both fish populations and fishing fleets targeting them. Appropriate management measures have to be found in order to reduce these risks and decrease sensitivity to uncertainties. Methods have been developed within decision theory that aim at allowing decision making under severe uncertainty. One of these methods is the information-gap decision theory. The info-gap method has started to permeate ecological modelling, with recent applications to conservation. However, these practical applications have so far been restricted to simple models with analytical solutions. Here we implement a deterministic approach based on decision theory in a complex model of the Eastern English Channel. Using the ISIS-Fish modelling platform, we model populations of sole and plaice in this area. We test a wide range of values for ecosystem, fleet and management parameters. From these simulations, we identify management rules controlling fish harvesting that allow reaching management goals recommended by ICES (International Council for the Exploration of the Sea) working groups while providing the highest robustness to uncertainties on ecosystem parameters.     

Modeling of signal-response cascades using decision tree analysis

MOTIVATION: Signal transduction cascades governing cell functional responses to stimulatory cues play crucial roles in cell regulatory systems and represent promising therapeutic targets for complex human diseases. however, mathematical analysis of how cell responses are governed by signaling activities is challenging due to their multivariate and non-linear nature. diverse computational methods are potentially available, but most are ineffective for protein-level data that is limited in extent and replication. Results: We apply a decision tree approach to analyze the relationship of cell functional response to signaling activity across a spectrum of stimulatory cues. as a specific example, we studied five intracellular signals influencing fibroblast migration under eight conditions: four substratum fibronectin levels and presence versus absence of epidermal growth factor. we propose techniques for preprocessing and extending the experimental measurement set via interpolative modeling in order to gain statistical reliability. for this specific case study, our approach has 70% overall classification accuracy and the decision tree model reveals insights concerning the combined roles of the various signaling activities in governing cell migration speed. we conclude that decision tree methodology may facilitate elucidation of signal-response cascade relationships and generate experimentally testable predictions, which can be used as directions for future experiments.     

Breaking out from a restricted range: Alternative habitat models to assess population perspectives

Habitat conservation for restricted-range species should also consider adjacent areas, but the analytical approaches for such assessments (particularly for a future perspective) are constrained by currently observed habitat relationships. We used two conceptually different habitat modelling approaches for analysing habitat distribution for the isolated Estonian population of a species of European conservation concern, the Siberian flying squirrel (Pteromys volans (Linnaeus, 1758)). We expected that the correlative (statistical) approaches based on current location data will increasingly deviate along with the distance from the current range, compared with a mechanistic approach based on limiting factors for the species. For conservation planning, we also investigated how the current protected area network covers quality habitats around the current range. We constructed three alternative correlative models (MaxEnt; Random forest; Generalized Boosted Regression) utilizing remote-sensing (Sentinel-2; LiDAR) and forest inventory data for 1299 occurrences in the currently occupied ca. 1400 km² range. A mechanistic model was constructed as a decision tree that distinguished 11 quality classes of forest land based on the ecological prioritization of limiting factors: site type; forest cover; abundance of key tree species; stand age; patch size; and layer structure. Supporting our expectation, an overall good accordance of habitat predictions of all the correlative models and the mechanistic model (at 30 × 30 m pixel size) declined with the distance from the current range. The MaxEnt model most closely followed the full range of habitat quality classes of the mechanistic model, while the other correlative models emphasized the highest habitat-quality class. Within the current range, both MaxEnt and the mechanistic model similarly revealed habitat quality differences between occupied and unoccupied species protection areas. Delineation of habitat aggregations all over the country based on the mechanistic model revealed habitat loss both within and adjacent to the current range, which sets limits to local population recovery. For analysing wider options, we recommend complementing statistical spatial modelling of current conditions with ecologically sound mechanistic approaches. Based on our specific case, we outline how such model predictions can be assessed for management planning beyond current range.     

An Efficient Heuristic Approach to Recognize the Infeasibility of a Loading Problem

The success of hierarchical production planning approaches for flexible manufacturing systems lies in the consistency of decision outcomes at various decision levels. For instance, the loading problem, which is solved at a lower level, may not yield a feasible loading solution to a set of part types selected at a higher level. This paper attemps to address the issue of recognizing the infeasibility of a loading solution. We present a modified loading model that includes a penalty for each operation not assigned to any machine. We develop a Lagrangian-based heuristic procedure and provide a sufficient condition on the quality of heuristic solutions that, if satisfied, will enable us to use the heuristic solutions to recognize the infeasibility of a loading problem. The proposed model and the dual-based heuristic can be effectively incorporated in an FMS hierarchical production planning approach that finds a good loading solution by iteratively comparing different part grouping scenarios.     

How good are Bayesian belief networks for environmental management? A test with data from an agricultural river catchment

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Predicting the unexpected: using a qualitative model of a New Zealand dryland ecosystem to anticipate pest management outcomes

Pest management is expensive and there is often uncertainty about the benefits for the resources being protected. There can also be unintended consequences for other parts of the ecosystem, especially in complex food webs. In making decisions managers generally have to rely on qualitative information collected in a piecemeal fashion. A method to assist decision making is a qualitative modelling approach using fuzzy cognitive maps, a directed graphical model related to neural networks that can take account of interactions between pests and conservation assets in complex food webs. Using all available information on relationships between native and exotic resources and consumers, we generated hypotheses about potential consequences of single‐species and multi‐species pest control on the long‐term equilibrium abundances of other biotic components of an ecosystem. We applied the model to a dryland ecosystem in New Zealand because we had good information on its trophic structure, but the information on the strength of species interactions was imprecise. Our model suggested that pest control is unlikely to significantly boost native invertebrates and lizards in this ecosystem, suggesting that other forms of management may be required for these groups. Most of the pest control regimes tested resulted in greater abundances of at least one other pest species, which could potentially lead to other management problems. Some of the predictions were unexpected, such as more birds resulting from possum and mouse control. We also modelled the effects of an increase in invasive rabbits, which led to unexpected declines of stoats, weasels, mice and possums. These unexpected outcomes resulted from complex indirect pathways in the food web. Fuzzy cognitive maps allow rapid construction of prototype models of complex food webs using a wide range of data and expert opinion. Their utility lies in providing direction for future monitoring efforts and generating hypotheses that can be tested with field experiments.     

干旱地区经济-生态环境系统规划方法与应用

从干旱地区经济－生态环境系统的动态性,综合性,多目标性和不确定性等特征出发,提出用不确定性多目标规划（IMOP）模型来解决干旱地区以水资源为核心的生态环境规划优化问题,建立了干旱地区经济与生态环境系统不确定性多目标规划模型（IMOPMEES）,应用模型算法退化形式进行多目标规划的求解,并以新疆和墨洛地区为具体研究对象,采用IMOPMEES进行可持续发展规划,应用交互式调整和情景分析的方法得到模型在两种情景下的优化方案,进而对两种情景进行综合分析比较,为决策者提供科学合理的决策依据,研究表明IMOP模型可以充分反映干旱地区经济－生态环境系统的不确定性和动态性,并有效地协调目标之间的冲突,为干旱地区经济－生态环境规划工作提供了一种较为有力的工具。     

Incorporating uncertainty and social values in managing invasive alien species: a deliberative multi-criteria evaluation approach

The management of Invasive Alien Species (IAS) is stymied by complex social values and severe levels of uncertainty. However, these two challenges are often hidden in the conventional model of management by “value-free” analyses and probability-based estimates of risk. As a result, diverse social values and wide margins of error in risk assessment carry zero weights in the decision-making process, leaving IAS risk decisions to be made in the wake of political pressure and the crisis atmosphere of incursion. We propose to use a Deliberative Multi-Criteria Evaluation (DMCE) to incorporate multiple social values and profound uncertainty into decision-making processes. The DMCE process combines the advantages of conventional multi-criteria decision analysis methods with the benefits of stakeholder participation to provide an analytical structure to assess complex multi-dimensional objectives. It, therefore, offers an opportunity for diverse views to enter the decision-making process, and for the negotiation of consensus positions. The DMCE process can also function as a platform for risk communication in which scientists, stakeholders, and decision-makers can interact and discuss the uncertainty associated with biological invasions. We examine two case studies that demonstrate how DMCE provides scientific rigor and transparency in the decision-making process of invasion risk management. The first case regards pre-border priority ranking for potential invasive species and the second relates to selecting the most desirable policy option for managing a post-border invader.     

基于LUCC的生态系统服务空间化研究——以张掖市甘州区为例

生态系统服务(ES)评价为减缓或阻止人类活动导致的各种复杂生态问题提供了一种可能的方法,同时也有助于解决日益增加的各种土地利用冲突.以张掖市甘州区为例,运用一种新的空间化方法开展生态系统服务研究,并注重实现与土地利用研究的集成分析.首先根据研究区不同的群落生境和土地利用类型划分生态系统服务类型,然后检验不同土地利用类型和方式对各类生态系统服务供给的影响,最后分析该方法和数据的不确定性.结果表明:1)在灌区和甘州区两个尺度上,4类ES生产能力值均表现为:文化服务＞支持服务＞调节服务＞供给服务;2)2000-2009年,4类ES供给都呈递减趋势,同时,城镇用地、路网建设等人类活动驱动的土地利用方式迅速增加,整个绿洲农业区处于过度开发状态;3)该方法可操作性强,多学科的数据和知识分析是ES研究的难点.     

A weighted partial likelihood approach for zero‐truncated models

Zero‐truncated data arises in various disciplines where counts are observed but the zero count category cannot be observed during sampling. Maximum likelihood estimation can be used to model these data; however, due to its nonstandard form it cannot be easily implemented using well‐known software packages, and additional programming is often required. Motivated by the Rao–Blackwell theorem, we develop a weighted partial likelihood approach to estimate model parameters for zero‐truncated binomial and Poisson data. The resulting estimating function is equivalent to a weighted score function for standard count data models, and allows for applying readily available software. We evaluate the efficiency for this new approach and show that it performs almost as well as maximum likelihood estimation. The weighted partial likelihood approach is then extended to regression modelling and variable selection. We examine the performance of the proposed methods through simulation and present two case studies using real data.     

Incorporating ecological functions in conservation decision making

Systematic conservation planning has become a standard approach globally, but prioritization of conservation efforts hardly considers species traits in decision making. This can be important for species persistence and thus adequacy of the conservation plan. Here, we developed and validated a novel approach of incorporating trophic information into a systematic conservation planning framework. We demonstrate the benefits of this approach using fish data from Europe's second largest river, the Danube. Our results show that adding trophic information leads to a different spatial configuration of priority areas at no additional cost. This can enhance identification of priority refugia for species in the lower position of the trophic web while simultaneously identifying areas that represent a more diverse species pool. Our methodological approach to incorporating species traits into systematic conservation planning is generally applicable, irrespective of realm, geographical area, and species composition and can potentially lead to more adequate conservation plans.     

Extended statistical approaches to modelling spatial pattern in biodiversity in northeast New South Wales. I. Species-level modelling

Statistical modelling of biological survey data in relation to remotely mapped environmental variables is a powerful technique for making more effective use of sparse data in regional conservation planning. Application of such modelling to planning in the northeast New South Wales (NSW) region of Australia represents one of the most extensive and longest running case studies of this approach anywhere in the world. Since the early 1980s, statistical modelling has been used to extrapolate distributions of over 2300 species of plants and animals, and a wide variety of higher-level communities and assemblages. These modelled distributions have played a pivotal role in a series of major land-use planning processes, culminating in extensive additions to the region's protected area system. This paper provides an overview of the analytical methodology used to model distributions of individual species in northeast NSW, including approaches to: (1) developing a basic integrated statistical and geographical information system (GIS) framework to facilitate automated fitting and extrapolation of species models; (2) extending this basic approach to incorporate consideration of spatial autocorrelation, land-cover mapping and expert knowledge; and (3) evaluating the performance of species modelling, both in terms of predictive accuracy and in terms of the effectiveness with which such models function as general surrogates for biodiversity.     

Practical tool for landscape planning? An empirical investigation of network based models of habitat fragmentation

This study presents a graph-theoretical modelling approach using daily movements and habitat demands of different target bird species in an urban context to assess: 1) habitable land cover types, 2) threshold distances between patches of habitat, 3) the required minimum accessible habitat areas and 4) the effects of barriers and stepping stones. The modelling approach is tested using empirical data from field surveys in the urban area of Stockholm, Sweden.
The results show that groups of small habitat patches can house the same species as larger contiguous patches as long as they are perceived as functionally connected by the inhabitant organisms. Furthermore, we found that binary habitat/non-habitat representations of the landscape could roughly explain the variation in species occurrence, as long as habitat was properly defined. However, the explanatory power of the landscape models increased when features of matrix heterogeneity such as stepping stones and barriers were accounted for.
Synthesis and application: in a world where forest ecosystems are becoming increasingly fragmented there is an urgent need to find comprehensive and scientifically relevant methods for managing and planning ecosystems. This study shows that: 1) groups of well placed small habitat patches can, together, be sufficient to attract birds in intensively developed areas, 2) the presented modelling approach can help identify such groups of patches, 3) matrix heterogeneity should preferably be accounted for, and 4) proper assessments of habitable land cover types are important. Finally, we argue that the modelling approach applied here may substantially improve landscape management and planning at scales ranging from whole landscapes down to neighbourhoods.     

An integrative methodology to predict dispersal of genetically modified genotypes in oilseed rape at landscape-level—A study for the region of Schleswig-Holstein,Germany

Potential environmental as well as socio-economic effects of the cultivation of genetically modified (GM) oilseed rape (OSR) may be caused by large-scale dispersal of transgenes. We present an up-scaling approach that was based on scenario assumptions concerning the percentage of GM cultivation and took into account natural and anthropogenic variation of involved dispersal processes. The applied methods include computer modelling and spatial analysis. A simulation model (GeneTraMP) was used to calculate the spatio-temporal pattern of the spread of a neutral transgene (without any specific function) in OSR. Basic scenario calculations were carried out for different spatial configurations covering 1 km² each and taking into account information on climate and cultivation systems of the region of the federal state of Schleswig-Holstein, Germany. For the exemplary regional study presented here, we analysed the numbers of flowering plants of GM OSR in different types of locations as predicted by the model. The results confirmed the expectation of a very high variability of GM occurrences at distinguishable intensity levels which were closely related to the proximity of areas of intended GM oilseed rape cultivation and may be described by a combination of management parameters and location type.The up-scaling method included a spatial analysis of the target region. Based on satellite images and digital maps, the structure of the region was analysed resulting in a map of Schleswig-Holstein that represents each single field, also including information on crop rotation, ownership and production systems. Applying GIS queries to this database, we identified the area of relevant location types. Both, the model results and the spatial data were used to predict the total numbers of flowering GM OSR plants for the region of Schleswig-Holstein. As an important feature, the up-scaling of modelling results to a larger scale allows for a comprehensive analysis by also enclosing regional parameters, as, for example the cropping density. The presented methods can support decision making if they are incorporated into the planning of an environmental monitoring of commercial GM crops or into life cycle assessment and cost–benefit analyses of GMO cultivation.     

Consistent micro, macro and state-based population modelling

A population system can be modelled using a micro model focusing on the individual entities, a macro model where the entities are aggregated into compartments, or a state-based model where each possible discrete state in which the system can exist is represented. However, the concepts, building blocks, procedural mechanisms and the time handling for these approaches are very different. For the results and conclusions from studies based on micro, macro and state-based models to be consistent (contradiction-free), a number of modelling issues must be understood and appropriate modelling procedures be applied. This paper presents a uniform approach to micro, macro and state-based population modelling so that these different types of models produce consistent results and conclusions. In particular, we demonstrate the procedures (distribution, attribute and combinatorial expansions) necessary to keep these three types of models consistent. We also show that the different time handling methods usually used in micro, macro and state-based models can be regarded as different integration methods that can be applied to any of these modelling categories. The result is free choice in selecting the modelling approach and the time handling method most appropriate for the study without distorting the results and conclusions.     

Fauna habitat modelling and mapping: A review and case study in the Lower Hunter Central Coast region of NSW

Abstract Habitat models are now broadly used in conservation planning on public lands. If implemented correctly, habitat modelling is a transparent and repeatable technique for describing and mapping biodiversity values, and its application in peri‐urban and agricultural landscape planning is likely to expand rapidly. Conservation planning in such landscapes must be robust to the scrutiny that arises when biodiversity constraints are placed on developers and private landholders. A standardized modelling and model evaluation method based on widely accepted techniques will improve the robustness of conservation plans. We review current habitat modelling and model evaluation methods and provide a habitat modelling case study in the New South Wales central coast region that we hope will serve as a methodological template for conservation planners. We make recommendations on modelling methods that are appropriate when presence‐absence and presence‐only survey data are available and provide methodological details and a website with data and training material for modellers. Our aim is to provide practical guidelines that preserve methodological rigour and result in defendable habitat models and maps. The case study was undertaken in a rapidly developing area with substantial biodiversity values under urbanization pressure. Habitat maps for seven priority fauna species were developed using logistic regression models of species‐habitat relationships and a bootstrapping methodology was used to evaluate model predictions. The modelled species were the koala, tiger quoll, squirrel glider, yellow‐bellied glider, masked owl, powerful owl and sooty owl. Models ranked sites adequately in terms of habitat suitability and provided predictions of sufficient reliability for the purpose of identifying preliminary conservation priority areas. However, they are subject to multiple uncertainties and should not be viewed as a completely accurate representation of the distribution of species habitat. We recommend the use of model prediction in an adaptive framework whereby models are iteratively updated and refined as new data become available.