Spatial modelling of socioeconomic data to understand patterns of human-caused wildfire ignition risk in the SW of Madrid (central Spain)

The majority of wildfires in the Mediterranean Basin are caused directly or indirectly by human activity. Many biophysical and socioeconomic factors have been used in quantitative analyses of wildfire risk. However, the importance and effects of socioeconomic factors in spatial modelling have been given inadequate attention. In this paper, we use different approaches to spatially model our data to examine the influence of human activity on wildfire ignition in the south west of the Madrid region, central Spain. We examine the utility of choropleth and dasymetric mapping with both Euclidean and functional distance surfaces for two differently defined wildfire seasons. We use a method from Bayesian statistics, the Weights of Evidence model, and produce ten predictive maps of wildfire risk: (1) five maps for a two-month fire season combining datasets of evidence variables and (2) five maps for the four-month fire season using the same dataset combinations. We find that the models produced from a choropleth mapping approach with spatial variables using Euclidian and functional distance surfaces are the best of the ten models. Results indicate that spatial patterns of wildfire ignition are strongly associated with human access to the natural landscape. We suggest the methods and results presented will be useful to optimize wildfire prevention resources in areas where human activity and the urban-forest interface are important factors for wildfire ignition.     

Assessing landscape values: a proposal for a multidimensional conceptual model

To evaluate a specific form of resource management, for example, the maintenance of valuable cultural landscapes, the contributions of many different viewpoints must be considered. In this paper, a model for assessing and integrating the different aspects contributing to landscape evaluation is proposed. Some results of its use in landscape assessment in the Madrid region (central Spain) are discussed.The model takes into account five assessment dimensions which are considered to be independent—the ecological, productive, economic, social and cultural evaluative systems and it recognises and assumes conflicts and trade-offs between these components. The inclusion of the production system as an independent assessment dimension is original and is justified because it is the link that connects the ecological and economic systems. Since this dimension acts most directly on the ecosystem, the production system includes the attribute of ecological or strong (in the sense of [For the Common Good, Beacon Press, Boston, 1994]) sustainability.The evaluation method places special emphasis on defining a conceptual framework, and from this defines scenarios in comparison with which particular development models (landscape planning, resources management) can be evaluated. Seven scenarios, coming from a hypothetical, traditional sustainable scenario of resources management, are defined. One of these, the sound sustainable scenario, is defended as the only one viable in the long term, particularly in developing countries.This method was employed to evaluate the agricultural and natural landscape of the Madrid region municipalities. The multivariate approach adopted was based on the selection of indicator sets for each evaluative dimension. The structure of relationships among indicators was then analysed separately for each dimension and values assigned to the municipalities according to their position on the main axes of the multivariate analyses. Each municipality was assigned to one of the seven development scenarios by means of discriminant analysis. The approach’s greatest assets are its flexibility in the selection of the indicators and the efficacy in its monitoring and comparison of the different analysed cases once a rigorous conceptual framework was established.The paper discusses the conditions for the sustainability of the human activities and provides a method for evaluating and comparing scenarios of resources management.     

Landscape network congruency at the ecoregion scale: can we expect corridor ‘umbrellas’?

Conservation planners are faced with numerous choices regarding ‘what to connect’ when modeling landscape networks on an ecoregion scale. A simplifying assumption is often that coarse-scale corridors may provide overlapping or ‘umbrella’ effects for multiple conservation scenarios. To examine this, we assessed differences in connectivity models arising from four different conservation scenarios for the transboundary Northern Appalachian/Acadian Ecoregion (330,000 km²). Models resulted in networks comprised of varying levels of connectedness, suggesting the presence of local connectivity at several sub-ecoregion scales, but diminished ecoregion-scale connectivity. Our results also indicate that selecting ‘what to connect’ at the ecoregion scale strongly influences the location and extent of modeled corridors. Further, our evidence suggests that landscape networks derived for one scenario are not likely to produce far-reaching corridor umbrellas or highly coincident landscape networks for alternative conservation scenarios with varying goals. As a result, alternative landscape networks should not be considered functionally equivalent.     

天宝岩自然保护区森林景观格局与环境关系的尺度效应分析

为揭示景观格局内演机制,在3S技术平台支撑下,借助TM遥感像图、植被分类图和二类调查数据,并引入CCA(Canonical Correspondence Analysis)排序方法,对天宝岩森林景观格局与环境的关系及其尺度效应进行定量分析.结果表明:随着取样尺度的递减,排序轴各特征值之和呈增大趋势,依次为:3.137、3.349、3.484、4.660及4.848;而累积景观–环境解释量和景观–环境相关系数皆呈先增后减的趋势,分别在scale3和scale2达到最大;景观类型分布呈现较为明显的纬度梯度变化规律(纬度与第一轴的相关系数:-0.2019~-0.3789)和垂直分布规律(海拔与第一轴的相关系数:-0.3218~-0.4760),其中沿纬度梯度上分布的景观类型依次为:竹林、落叶阔叶林、针阔混交林、农田、常绿阔叶林、山顶矮曲林、常绿针叶林、泥潭藓沼泽、灌草丛;在较低海拔上分布的景观类型主要有竹林和农田,在较高海拔上分布着泥潭藓沼泽、灌草丛和山顶矮曲林;景观格局与环境关系具有尺度效应,环境因子对景观格局分布的影响程度随着尺度的变化而变化.图2表2参36     

Exploring long-term land cover changes in an urban region of southern Europe

This paper addresses two important issues for large Mediterranean city regions: the differential impact of compact urban ‘growth’ and low-density ‘sprawl’ on land cover changes (LCCs), and their final effect on changing land cover relationships (LCRs). The urban expansion of Rome (Italy) during the last 50 years and the related LCCs were investigated as a paradigmatic example of compact versus dispersed urban development. LCCs were assessed over 5 years (1960, 1974, 1990, 2000, 2006) by analysing diachronically the distribution of 12 land cover categories derived from digital land cover maps covering the entire Nuts-3 prefecture of Rome (5353 km²). LCRs were studied using multi-way data analysis. LCCs were found to have relative differences during ‘growth’ (1960–1990) and ‘sprawl’ (1990–2006) phases. Conversion to urban land uses concentrated in the 1960s and 1970s at the urban fringe, while expanding progressively far from the city in the 1990s and 2000s. During the ‘growth’ phase, the land cover classes with the highest probability of being converted to urban uses were arable lands, annual crops, vineyards and pastures. During the ‘sprawl’ phase, olive groves, orchards and forest surfaces also decreased due to the development of low-density built-up areas and infrastructure. Planning suggestions aimed at mitigating the alteration of the rural landscape through sprawl conditions are discussed.     

Integrating Landscape and Metapopulation Modeling Approaches: Viability of the Sharp-Tailed Grouse in a Dynamic Landscape

Abstract:  The lack of management experience at the landscape scale and the limited feasibility of experiments at this scale have increased the use of scenario modeling to analyze the effects of different management actions on focal species. However, current modeling approaches are poorly suited for the analysis of viability in dynamic landscapes. Demographic (e.g., metapopulation) models of species living in these landscapes do not incorporate the variability in spatial patterns of early successional habitats, and landscape models have not been linked to population viability models. We link a landscape model to a metapopulation model and demonstrate the use of this model by analyzing the effect of forest management options on the viability of the Sharp-tailed Grouse (  Tympanuchus phasianellus ) in the Pine Barrens region of northwestern Wisconsin (U.S.A.). This approach allows viability analysis based on landscape dynamics brought about by processes such as succession, disturbances, and silviculture. The landscape component of the model (LANDIS) predicts forest landscape dynamics in the form of a time series of raster maps. We combined these maps into a time series of patch structures, which formed the dynamic spatial structure of the metapopulation component (RAMAS). Our results showed that the viability of Sharp-tailed Grouse was sensitive to landscape dynamics and demographic variables such as fecundity and mortality. Ignoring the landscape dynamics gave overly optimistic results, and results based only on landscape dynamics (ignoring demography) lead to a different ranking of the management options than the ranking based on the more realistic model incorporating both landscape and demographic dynamics. Thus, models of species in dynamic landscapes must consider habitat and population dynamics simultaneously.     

关帝山八水沟天然植物群落时空梯度的数量分析

在ＤＣＡ排序的基础上，应用排序轴分类法对山西关帝山八水沟景观的天然植物群落进行了数量分类，共划分出７类，效果较好．多元线性回归结果表明ＤＣＡ排序轴与环境因子密切相关．方差分析与多重比较结果表明，结合ＤＣＡ第２与３排序轴的二维排序图反映了群落的时空变异，其中第２排序轴反映了群落在坡向与坡度方面的空间梯度，第３排序轴反映了群落的森林演替、灌丛发育以及各类群落的草本层发育的时间梯度     

Are existing vegetation maps adequate to predict bird distributions?

Bird species are selective on the vegetation types in which they are found but predictive models of bird distribution based on variables derived from land-use/land-cover maps tend to have limited success. It has been suggested that accuracy of existing maps used to derive predictors is in part responsible for the limited success of bird distribution models. In two areas of 4900 km² of Western Andalusia, Spain, we compared the predictive ability of bird distribution models derived from two existing general-purpose land-use/land-cover maps, which differ in their resolution and accuracy: a coarse scale vegetation map of Europe, the CORINE land-cover map, and a detailed regional map, the 1995 land-use/land-cover map of Andalusia from the SINAMBA (Consejerı́a de Medio Ambiente, Junta de Andalucı́a). We compared the bird distribution models derived from these general-purpose vegetation maps with models derived from two more accurate structural vegetation maps built considering directly variables that influence bird habitat selection, one built from satellite images for this study and another obtained by improving the resolution and accuracy of the SINAMBA map with satellite data. We sampled the presence/absence of bird species at 857 points using 15-min point surveys. Predictive models for 54 bird species were built with generalised additive models (GAMs), using as potential predictors the same set of landscape and vegetation structure variables measured on each map. We compared for each bird species the predictive accuracy of the best model derived from each map. Vegetation structure measured at bird sample points was used as ground-truth for comparing the accuracy of vegetation maps. Although maps differed in their resolution and accuracy, the results show that all of them produced similarly accurate bird distribution models, with a mixed map produced with both thematic and satellite information being the best. The models derived from the more accurate vegetation structure maps obtained from satellite data were not more accurate than those derived directly from the SINAMBA or CORINE maps. Our results suggest that some general-purpose land-use/land-cover maps are accurate enough to derive bird distribution models. There is a certain limit to improve vegetation maps above which there is no effect in their power to predict bird distribution.     

Simulating spatiotemporal dynamics of urbanization with multi-agent systems—A case study of the Phoenix metropolitan region, USA

Urbanization is a human-dominated process and has greatly impacted biodiversity, ecosystem processes, and regional climate. To understand the socioeconomic drivers of urbanization and project future urban landscape changes, multi-agent systems provide a powerful tool. We develop an agent-based model of urban growth for the Phoenix metropolitan region of the United States, which simulates the behavior of regional authorities, real estate developers, residents, and environmentalists. The BDI (Beliefs-Desires-Intentions) structure is employed to simulate the agents behavior and decision models. The heterogeneity of agents is reflected by adjusting parameters according to the agents’ beliefs, desires and preferences. Three scenarios, baseline, economic development priority and environmental protection, are developed and analyzed. The combination of multi-agent system and spatial regression model is employed to predict the future urban development of the Phoenix metropolitan region. Landscape metrics are used to compare the spatial patterns of the urban landscape resulting from different scenarios in different times. In general, with the rapid urban expansion, the shape of urban patches will become more regular as many of them become coalesced. The spatial analysis of urban development through modeling individual and group decisions and human-environment interactions with a multi-agent systems approach can enhance our understanding of the socioeconomic driving forces and mechanisms of urban development.     

Plant competition varies with community composition in an edaphically complex landscape

There is currently no consensus on how physical and biological factors affect competitive intensity. Tests of whether competitive intensity varies along axes of environmental change have commonly been conducted in systems with a single strong environmental gradient, such as productivity, a soil resource, or an environmental stress. Frequently, these same axes are associated with changes in species composition, yet few studies have asked whether shifts in the identity of competitors affect competitive intensity. We ask whether resources (nutrients, water), stressors (heavy metals, Ca:Mg ratio), productivity (aboveground biomass), or species identity (an ordination axis of plant community composition) were the best predictors of the intensity of competition in a heterogeneous grassland landscape that included multiple independent environmental gradients. The reproductive fitness of six annual plant species was measured in the presence and absence of competitors and used to calculate relative interaction intensity (RII). We found that RII was best predicted by community composition. Nutrient availability was also important, and a post hoc test showed that competitive intensity was best explained by the combined effects of community composition and nutrient availability. We argue that community composition may be the most effective metric for predicting competitive intensity in many ecosystems because it includes both the competitive effects of the local community and information about covarying environmental characteristics.     

Constrained additive ordination

For several decades now, ecologists have sought to determine the shape of species' response curves and how they are distributed along unknown underlying gradients, environmental latent variables, or ordination axes. Its determination has important implications for both continuum theory and community analysis because many theories and models in community ecology assume that responses are symmetric and unimodal. This article proposes a major new technique called constrained additive ordination (CAO) that solves this problem by computing the optimal gradients and flexible response curves. It allows ecologists to see the response curves as they really are, against the dominant gradients. With one gradient, CAO is a generalization of constrained quadratic ordination (CQO; formerly called canonical Gaussian ordination or CGO). It supplants symmetric bell-shaped response curves in CQO with completely flexible smooth curves. The curves are estimated using smoothers such as the smoothing spline. Loosely speaking, CAO models are generalized additive models (GAMs) fitted to a very small number of latent variables. Being data driven rather than model driven, CAO allows the data to "speak for itself" and does not make any of the assumptions made by canonical correspondence analysis. The new methodology is illustrated with a hunting spider data set and a New Zealand tree species data set.     

An interval joint-probabilistic programming method for solid waste management: a case study for the city of Tianjin, China

Currently, environmental protection and resources conservation continue to be challenges faced by solid-waste managers in China. These challenges are being further compounded by rapid socioeconomic devel- opment and population growth associated with increased waste generation rates and decreased waste disposal capacities. In response to these challenges, an interval joint-probabilistic mixed-integer programming （IJMP） method is developed for supporting long-term planning of waste management activities in the city of Tianjin, which is one of the largest municipalities in the northern part of China. In the IJMP, joint probabilistic constraints are introduced into an interval-parameter mixed-integer programming framework, such that uncertainties presented in terms of interval values and random variables can be reflected. Moreover, a number of violation levels for the waste-management-capacity constraints are examined, which can facilitate in-depth analyses of tradeoffs among economic objective and system-failure risk. The results indicate that reasonable solutions have been generated. They are valuable for supporting the adjustment of the city＇s existing waste-management practices and the long- term planning of the city＇s waste-management facilities.     

Environmental drivers of decadal change of a mangrove forest in the North coast of the Yucatan peninsula,Mexico

Mangrove forests provide important ecosystem services, but are under constant pressure from natural, anthropogenic, and climate change related disturbances. Environmental drivers on mangrove change at large spatial scales, other than sea level rise, are not well understood. In here, we use spatially explicit methods to identify the main environmental drivers of mangrove coverage change over a decade in the landscape of the North coast of the Yucatan peninsula, Mexico. A post-supervised classification approach on seven SPOT 5 multispectral satellite images was used to construct thematic maps of mangrove coverage between 2004 and 2014. A linear regression model between the thematic maps was performed to estimate the mangrove coverage change rate per pixel. Climate surfaces for annual maximum, minimum and mean temperature, and annual mean and cumulative precipitation for the region were calculated for the period 1980–2009 using data obtained from the National Meteorological Service. The effect of environmental variables on mangrove coverage change rates was assessed with a boosted generalized additive model (boosted GAM). The lowest and highest overall accuracy obtained for the time series thematic maps were 87.14% (Kappa?=?0.78), and 97.5% (Kappa?=?0.95), respectively. The most influential environmental variables on mangrove coverage change were annual cumulative precipitation (21%), and annual maximum temperature (9%). Current climate change scenarios for the region predict an increase in temperature and a decrease in precipitation, intensifying environmental stress on this ecosystem. Therefore, adequate management strategies are fundamental to help maintain the mangrove forest under changing environmental conditions.     

Land-use planning of Minoo Island,Iran, towards sustainable land-use management

Land-use planning is one aspect of sustainable development that determines the integrity of socioeconomic and ecological nuance. Land evaluation is an approach to sustainable land-use planning, which predicts the capability of the land-use system. This means that a specific land area should be under specific use, considering certain factors and characteristics of the land and its location. In other words, lack of proper information on land capability and rational and irrational use of land leads to destruction of land resources. This paper discusses the findings of ecological and socioeconomic resource issues for Minoo Island and the surrounded zone. The study covers an area of about 172km² in southwest Iran. To establish environmental unit maps, digital layer maps of the soil texture were overlapped. The study empirically established that the area has the capability to accommodate ‘levels two’ urban and industrial development. Moreover, Minoo Island is considered to have potential for conservation and ecotourism, with a 60% potentiality for dryland farming.     

Neustonic distributions

In order to determine the effects of environmental parameters on neustonic population structure, correspondence analysis, a multivariate technique of ordination, was applied to data on 47 taxa of neustonic animals, collected at 149 stations between Fiji and the Bay of Biscay from August, 1979 to December, 1980. The positions of taxa and samples on the first 3 axes extracted from the similarity matrix were related to environmental variables. The 3 variables which exerted the greatest influence on neustonic distributions were the quantities of floating tar, plastic and pumice, time of day, and surface water temperature. Floating abiotic material provided settlement areas for epifauna, particularly in the Mediterranean (tar and plastic) and the western Pacific (pumice). Many taxa were more numerous in the surface waters at night. Only 3 of the neustonic taxa examined were most common in boreal waters, all others were primarily tropical or sub-tropical in distribution. The taxa formed 8 associations of fauna which had different distribution patterns.     

Effects of Climate Change on Population Persistence of Desert-Dwelling Mountain Sheep in California

Abstract:  Metapopulations may be very sensitive to global climate change, particularly if temperature and precipitation change rapidly. We present an analysis of the role of climate and other factors in determining metapopulation structure based on presence and absence data. We compared existing and historical population distributions of desert bighorn sheep ( Ovis canadensis ) to determine whether regional climate patterns were correlated with local extinction. To examine all mountain ranges known to hold or to have held desert bighorn populations in California and score for variables describing climate, metapopulation dynamics, human impacts, and other environmental factors, we used a geographic information system (GIS) and paper maps. We used logistic regression and hierarchical partitioning to assess the relationship among these variables and the current status of each population (extinct or extant). Parameters related to climate—elevation, precipitation, and presence of dependable springs—were strongly correlated with population persistence in the twentieth century. Populations inhabiting lower, drier mountain ranges were more likely to go extinct. The presence of domestic sheep grazing allotments was negatively correlated with population persistence. We used conditional extinction probabilities generated by the logistic-regression model to rank native, naturally recolonized, and reintroduced populations by vulnerability to extinction under several climate-change scenarios. Thus risk of extinction in metapopulations can be evaluated for global-climate-change scenarios even when few demographic data are available.     

Ecological Correlates of Extinction Proneness in Australian Tropical Rain Forest Mammals

Abstract. There is a dire need to predict the vulnerability of tropical forest biotas to habitat fragmentation I tested the efficacy of seven ecological traits (body size Iongeuity, fecundity, trophic level, dietary specialization, natural abundance in rain forest and abundance in the surrounding habitat matrix) for predicting responses of 16 nonflying mammal species to rain forest fragmentation in tropical QueenslaM Australia An ordination analysis revealed that most (84%) of the variation in traits was described by two axes, the first separating rand K-selected species, and the second discriminating rare species with specialized diets from common species with generalized diets.
Using multiple regression analysis, the two ordination axes explained 51.7% of variation in mammal extinction proneness (F = 9.96 P = 0.009). Howem, univariate tests revealed tbat a single trait abundance in the mawas a betterpredictor of vulnerability (r²= 63.8%, F = 24.69, P < 0.001). Partial correlations demonstrated that once the effects of matrix abundance tuete remove4 no other traits or ordination axes were significant predictors of extinction proneness.
These results highlight the importance of tolerance of modijied habitats in determining survival of nonpying mammals in tropical forest fragments. Species tbat traverse or exploit modaxied habitats tend to remain stable or inmase in fragments whereas those tbat avoid these habitats often disappem The implications of these findings for hopical forest conservation are discussed.     

A review of prehistoric and early historic mainland salt marsh vegetation in the northern-Netherlands based on the analysis of plant macrofossils

The article presents an overview of archaeobotanical research on artificial dwelling mounds, so-called ‘terps’, in the northern-Netherlands. A total of 40 studies carried out over the past 40 years is evaluated. The vegetation diversity in the area as well as the differences with the present marsh are studied. Seriation, Principal Component Analysis and Sørensen similarity indices are used to assess the diversity of both individual samples and sites. For comparison with the present marshes, an index based on the TMAP vegetation typology was defined. Based on these methods, a selection of the individual samples was analyzed phytosociologically. It is found that all samples represent a mixture of vegetation types, but that the salt marsh species are a constant factor. The variation in the sample composition is not related to their dating, except for some of the latest samples that reflect the earliest endikements. Great similarity to the present marshes is found, but the analysis also testifies of a landscape profoundly disturbed by human activities throughout history.     

Quantifying inconsistencies in old cadastral maps and their impact on land-use reconstructions

ABSTRACT

Old cadastral maps represent a historical reference dataset for long-term land-use reconstructions. This study presents identification of inconsistencies in the nineteenth century Franziscean cadastre, one of the largest sets of old cadastral maps worldwide, by comparing three versions of the maps and written documents created in the same period. We identified all parcels and their land-use in the four sub-sources in six study areas. The overall share of inconsistencies among 5 771 identified parcels is 7.4%, with the biggest share of inconsistency in agroforestry and forestry classes. The most frequent inconsistencies are of ‘Not differentiable land use’ (n = 212) and ‘Different land-use’ categories across the sub-sources (n = 113). We conclude that the frequency of uncertainties in old cadastral maps may limit the validity of historical land-use reconstructions, affecting the eventual restoration and management efforts based on such data. We provide a summary for the use of Franziscean cadastre.