Socio-economic predictors of domestic wood use in an Atlantic forest area (north-east Brazil): a tool for directing conservation efforts

Given the excessive use pressure on forests, characterisation of the major users of forest resources can help guide conservation efforts. This study examines whether socio-economic variables explain domestic wood use in an area of Atlantic forest in the community of Três Ladeiras (Pernambuco, north-east Brazil). Socio-economic data were collected from 62 households, and geometric measurements of wood products in each household were taken to calculate the wood volume. An informant in each household indicated the average replacement time for each type of wood use. Data were analysed for static volume (wood volume found at the time of the visit) and dynamic volume (rate of wood consumption, calculated as the ratio between volume and replacement time). Multiple regression analyses were performed to determine the relationships between wood volume (static and dynamic) and the number of species used; independent variables including monthly family income, number of residents, average age of heads of the household, age of oldest household member, average residence time of heads of the household, average education level of heads of the household and education level of the most educated household member. Monthly income was inversely related to the static wood volume (adjusted R ² = 17.6%, p < 0.001), rate of wood consumption (adjusted R ² = 23.0%, p < 0.001) and number of species used (adjusted R ² = 22.3%, p < 0.001) and was the most important predictive variable for these three outcomes. Other factors such as average education level and average age of heads of the household were also explanatory variables of the total rate of wood consumption, as these two variables, together with monthly family income, explain 31.3% of the total dynamic volume. Given the high importance of income in our study, we suggest that future conservation strategies give special attention to the poorest members of the community.     

Measuring impacts on species with models and metrics of varying ecological and computational complexity

Approaches to assess the impacts of landscape disturbance scenarios on species range from metrics based on patterns of occurrence or habitat to comprehensive models that explicitly include ecological processes. The choice of metrics and models affects how impacts are interpreted and conservation decisions. We explored the impacts of 3 realistic disturbance scenarios on 4 species with different ecological and taxonomic traits. We used progressively more complex models and metrics to evaluate relative impact and rank of scenarios on the species. Models ranged from species distribution models that relied on implicit assumptions about environmental factors and species presence to highly parameterized spatially explicit population models that explicitly included ecological processes and stochasticity. Metrics performed consistently in ranking different scenarios in order of severity primarily when variation in impact was driven by habitat amount. However, they differed in rank for cases where dispersal dynamics were critical in influencing metapopulation persistence. Impacts of scenarios on species with low dispersal ability were better characterized using models that explicitly captured these processes. Metapopulation capacity provided rank orders that most consistently correlated with those from highly parameterized and data-rich models and incorporated information about dispersal with little additional computational and data cost. Our results highlight the importance of explicitly considering species’ ecology, spatial configuration of habitat, and disturbance when choosing indicators of species persistence. We suggest using hybrid approaches that are a mixture of simple and complex models to improve multispecies assessments.     

Application of Remote Sensing Detection and GIS in Analysis of Vegetation Pattern Dynamics in the Yellow River Delta

Abstract

Regional vegetation pattern dynamics has a great impact on ecosystem and climate change. Remote sensing data and geographical information system (GIS) analysis were widely used in the detection of vegetation pattern dynamics. In this study, the Yellow River Delta was selected as the study area. By using 1986, 1993, 1996, 1999 and 2005 remote sensing data as basic information resource, with the support of GIS, a wetland vegetation spatial information dataset was built up. Through selecting the land-scape metrics such as class area (CA), class percent of landscape (PL), number of patch (NP), largest patch index (LPI) and mean patch size (MPS) etc., the dynamics of vegetation pattern was analyzed. The result showed that the change of vegetation pattern is significant from 1986 to 2005. From 1986–1999, the area of the vegetation, the percent of vegetation, LPI and MPS decreased, the NP increased, the vegetation pattern tends to be fragmental. The decrease in vegetation area may well be explained by the fact of the nature environment evolution (Climate change and decrease in Yellow River runoff) and the increase in the population in the Yellow River Delta. However, from 1999–2005, the area of the vegetation, the percent of vegetation, LPI and MPS increased, while the NP decreased. This trend of restoration may be due to the implementation of water resources regulation for the Yellow River Delta since 1999.     

Urbanization and Its Effect On Runoff in the Whiteoak Bayou Watershed,Texas1

Abstract: The capacity of a watershed to urbanize without changing its hydrologic response and the relationship between that response and the spatial configuration of the developed areas was studied. The study was conducted in the Whiteoak Bayou watershed (223 km²), located northwest of Houston, Texas, over an analysis period from 1949 to 2000. Annual development data were derived from parcel data collected by the Harris County Appraisal District. Using these data, measures of the spatial configuration of the watershed urban areas were calculated for each year. Based on regression models, it was determined that the annual runoff depths and annual peak flows depended on the annual precipitation depth, the developed area and the maximum 12‐h precipitation depth on the day and day before the peak flow took place. It was found that, since the early 1970s, when the watershed reached a 10% impervious area, annual runoff depths and peak flows have increased by 146% and 159%, respectively. However, urbanization is responsible for only 77% and 32% of the increase, respectively, while precipitation changes are responsible for the remaining 39% and 96%, respectively. Likewise, an analysis of the development data showed that, starting in the early 1970s, urbanization in the watershed consisted more of connecting already developed areas than of creating new ones, which increases the watershed’s conveyance capacity and explains the change in its response. Before generalizing conclusions, though, further research on other urban watersheds with different urbanization models appears to be necessary.     

Forecasts of habitat loss and fragmentation due to urban growth are sensitive to source of input data

The conversion of natural habitat to urban settlements is a primary driver of biodiversity loss, and species' persistence is threatened by the extent, location, and spatial pattern of development. Urban growth models are widely used to anticipate future development and to inform conservation management, but the source of spatial input to these models may contribute to uncertainty in their predictions. We compared two sources of historic urban maps, used as input for model calibration, to determine how differences in definition and scale of urban extent affect the resulting spatial predictions from a widely used urban growth model for San Diego County, CA under three conservation scenarios. The results showed that rate, extent, and spatial pattern of predicted urban development, and associated habitat loss, may vary substantially depending on the source of input data, regardless of how much land is excluded from development. Although the datasets we compared both represented urban land, different types of land use/land cover included in the definition of urban land and different minimum mapping units contributed to the discrepancies. Varying temporal resolution of the input datasets also contributed to differences in projected rates of development. Differential predicted impacts to vegetation types illustrate how the choice of spatial input data may lead to different conclusions relative to conservation. Although the study cannot reveal whether one dataset is better than another, modelers should carefully consider that geographical reality can be represented differently, and should carefully choose the definition and scale of their data to fit their research objectives.     

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.     

A multispecies test of source–sink indicators to prioritize habitat for declining populations

For species at risk of decline or extinction in source–sink systems, sources are an obvious target for habitat protection actions. However, the way in which source habitats are identified and prioritized can reduce the effectiveness of conservation actions. Although sources and sinks are conceptually defined using both demographic and movement criteria, simplifications are often required in systems with limited data. To assess the conservation outcomes of alternative source metrics and resulting prioritizations, we simulated population dynamics and extinction risk for 3 endangered species. Using empirically based habitat population models, we linked habitat maps with measured site‐ or habitat‐specific demographic conditions, movement abilities, and behaviors. We calculated source–sink metrics over a range of periods of data collection and prioritized consistently high‐output sources for conservation. We then tested whether prioritized patches identified the habitats that most affected persistence by removing them and measuring the population response. Conservation decisions based on different source–sink metrics and durations of data collection affected species persistence. Shorter time series obscured the ability of metrics to identify influential habitats, particularly in temporally variable and slowly declining populations. Data‐rich source–sink metrics that included both demography and movement information did not always identify the habitats with the greatest influence on extinction risk. In some declining populations, patch abundance better predicted influential habitats for short‐term regional persistence. Because source–sink metrics (i.e., births minus deaths; births and immigrations minus deaths and emigration) describe net population conditions and cancel out gross population counts, they may not adequately identify influential habitats in declining populations. For many nonequilibrium populations, new metrics that maintain the counts of individual births, deaths, and movement may provide additional insight into habitats that most influence persistence.     

Multi-temporal land use and cover changing analysis: the environmental impact in Mediterranean area

ABSTRACT

Human activity shapes the levels of anthropogenic pressure that depend on the land management method adopted. This has a fundamental role in the transformation of traditional landscapes. This study focuses on a representative region of the Mediterranean area with the objective to analyse the landscape’s dynamics, to detect the spatial arrangement of class patches, to identify the main agroecosystem characters and to provide a framework to assess ecosystems services. In order to assess land use/land cover changes and landscape persistence, the period between 1960 and 2012 was analysed, taking into consideration the years 1960, 2000 and 2012 using comparable land use maps. Land use and land cover analysis show an urban area growth of 24% during 2000–2012 and of 523% over between 1960 and 2012. The very high levels of land abandonment up to the year 2000 (+7216%) have reversed their trend between 2000 and 2012 (?95%). The orchards showed a relevant increase, particularly after 2000, while the vineyards were linked to the highest value of surface erosion (?74%). The outcomes showed that urban settlements can damage the ecological network with negative effects on the landscape’s environmental sustainability in proximity of significant urban centres. Instead, the ecological network is well preserved and highly associated to the agricultural areas when there is the persistence of many land uses and low urban density, despite the presence of dynamic changes.     

剧烈人类活动区经济发展与环境污染水平关系--以广东省东莞市为例

近年来,经济发展与环境污染之间的关系成了社会经济发展的热点问题。本研究选取东莞市1990-2010年经济与环境数据,探究环境库兹涅茨曲线演替轨迹,得出研究时段内东莞市环境库兹涅茨曲线大体呈“倒U型”特征,其中“倒U型”峰值大约出现在2007年人均GDP为4.51万元时;工业废水、工业废气以及工业废渣排放量3项指标的环境库兹涅茨曲线,分别呈现弱的“倒U型”、“倒U型上升阶段”、“倒U型＋倒U型上升阶段”特征。结果表明,东莞市环境库兹涅茨曲线特征与污染物排放量、产业结构、环保政策及投资等有较为密切的关系。结论建议：东莞市可以通过调整产业结构、加大环境保护投资力度等措施促进经济转型,加速“倒U型”曲线后半段的形成。     

基于OTIS模型的巢湖十五里河源头段氮磷滞留特征

为揭示巢湖十五里河源头河段氮磷营养盐滞留特征,选择NH4Cl、KH2PO4及NaCl作为示踪剂,开展野外现场示踪实验.根据获得的氯离子浓度和营养盐浓度穿透曲线数据信息,利用OTIS与OTIS-P计算软件,估算水文参数(D、A、As、a等)和营养盐一阶吸收系数(λ、λs).在此基础上,计算暂态存储指标和营养盐吸收相关参数值,定量评估主河道流动水体与暂态存储对NH4+和SRP的损失贡献.结果表明,主河道与暂态存储区NH4+吸收系数平均值分别为3.88×10-6,8.81×10-4s-1,SRP分别为7.80×10-6,7.98×10-8s-1;综合衰减系数k-NH4平均值为1.64×10-4s-1,k-SRP为7.80×10-6s-1;NH4+和SRP的吸收长度Sw-NH4、Sw-SRP相当大,分别为1632.88,25471.32m,意味着该河段经由物理或生态过程去除N、P的可能性低;该河段Vf-NH4、Vf-SRP 值较营养盐浓度低的溪流或小河流偏小,但U-NH4、U-SRP则明显偏大;暂态存储对于NH4+损失的平均贡献率为93.82%,主河道水流对SRP损失的平均贡献率高达99.70%.