土壤养分空间估测方法研究综述

土壤养分是土壤提供的植物生长发育所必需的营养元素。由于受到自然因素和人为因素的共同作用,土壤养分具有高度的空间变异性。土壤的这种特性不仅表现在区域尺度上,而且也表现在田块尺度上。在研究方法上经历了从传统统计学到地统计学,再到神经网络、地理信息技术以及高精度曲面建模等新方法的不断改进过程。文章从地统计学方法引入到土壤养分空间变异研究中为出发点,论述了国内外基于地统计学的土壤养分空间变异的研究现状,主要包括利用地统计学方法来确定合理的土壤采样数目,土壤养分空间变异的定量化研究,土壤养分空间变异的尺度效应;然后简述了神经网络、地理信息技术、高精度曲面建模等技术在土壤养分空间变异研究中的研究现状和应用。最后对比分析了各种研究方法在应用中存在的缺陷,同时指明了今后应加强作物生长的不同时期土壤养分的空间变异性、土壤在四维空间尺度上的演变机理以及环境信息获取的不确定性等方面的研究。     

Integrating intraseasonal grassland dynamics in cross-scale distribution modeling to support waterbird recovery plans

Despite much discussion about the utility of remote sensing for effective conservation, the inclusion of these technologies in species recovery plans remains largely anecdotal. We developed a modeling approach for the integration of local, spatially measured ecosystem functional dynamics into a species distribution modeling (SDM) framework in which other ecologically relevant factors are modeled separately at broad scales. To illustrate the approach, we incorporated intraseasonal water-vegetation dynamics into a cross-scale SDM for the Common Snipe (Gallinago gallinago), which is highly dependent on water and vegetation dynamics. The Common Snipe is an Iberian grassland waterbird characteristic of European agricultural meadows and a member of one of the most threatened bird guilds. The intraseasonal dynamics of water content of vegetation were measured using the standard deviation of the normalized difference water index time series computed from bimonthly images of the Sentinel-2 satellite. The recovery plan for the Common Snipe in Galicia (northwestern Iberian Peninsula) provided an opportunity to apply our modeling framework. Model accuracy in predicting the species’ distribution at a regional scale (resulting from integration of downscaled climate projections with regional habitat–topographic suitability models) was very high (area under the curve [AUC] of 0.981 and Boyce's index of 0.971). Local water-vegetation dynamic models, based exclusively on Sentinel-2 imagery, were good predictors (AUC of 0.849 and Boyce's index of 0.976). The predictive power improved (AUC of 0.92 and Boyce's index of 0.98) when local model predictions were restricted to areas identified by the continental and regional models as priorities for conservation. Our models also performed well (AUC of 0.90 and Boyce's index of 0.93) when projected to updated water-vegetation conditions. Our modeling framework enabled incorporation of key ecosystem processes closely related to water and carbon cycles while accounting for other factors ecologically relevant to endangered grassland waterbirds across different scales, allowed identification of priority areas for conservation, and provided an opportunity for cost-effective recovery planning by monitoring management effectiveness from space.     

Structurally-consistent estimation of use and nonuse values for landscape-wide environmental change

We address the problem of estimating the use and nonuse value derived from a landscape-wide programme of environmental change. Working in the random utility framework, we develop a structural model that describes both demand for recreational trips to the landscape's quality-differentiated natural areas and preferences over different landscape-wide patterns of environmental quality elicited in a choice experiment. The structural coherence of the model ensures that the parameters of the preference function can be simultaneously estimated from the combination of revealed and stated preference data. We explore the properties of the model in a Monte Carlo experiment and then apply it to a study of preferences for changes in the ecological quality of rivers in northern England. This implementation reveals plausible estimates of the use and nonuse parameters of the model and provides insights into the distance decay in those two different forms of value.     

Effects of spatial autocorrelation and sampling design on estimates of protected area effectiveness

Estimating the effectiveness of protected areas (PAs) in reducing deforestation is useful to support decisions on whether to invest in better management of areas already protected or to create new ones. Statistical matching is commonly used to assess this effectiveness, but spatial autocorrelation and regional differences in protection effectiveness are frequently overlooked. Using Colombia as a case study, we employed statistical matching to account for confounding factors in park location and accounted for for spatial autocorrelation to determine statistical significance. We compared the performance of different matching procedures—ways of generating matching pairs at different scales—in estimating PA effectiveness. Differences in matching procedures affected covariate similarity between matched pairs (balance) and estimates of PA effectiveness in reducing deforestation. Independent matching yielded the greatest balance. On average 95% of variables in each region were balanced with independent matching, whereas 33% of variables were balanced when using the method that performed worst. The best estimates suggested that average deforestation inside protected areas in Colombia was 40% lower than in matched sites. Protection significantly reduced deforestation, but PA effectiveness differed among regions. Protected areas in Caribe were the most effective, whereas those in Orinoco and Pacific were least effective. Our results demonstrate that accounting for spatial autocorrelation and using independent matching for each subset of data is needed to infer the effectiveness of protection in reducing deforestation. Not accounting for spatial autocorrelation can distort the assessment of protection effectiveness, increasing type I and II errors and inflating effect size. Our method allowed improved estimates of protection effectiveness across scales and under different conditions and can be applied to other regions to effectively assess PA performance.     

Using chemical models for evaluating the geochemical confinement capacity of a geological barrier application to the Centre de Stockage de l'Aube (France)

The feasibility of using a chemical reaction-based approach for evaluating and modelling the role of adsorption reactions in determining the geochernical confinement capacity of natural geological barriers is being studied as part of an on-going R & D programme. The confined superficial aquifer underlying the Centre de Stockage de l'Aube facility, a geological barrier for this site, has been used as a case study with the following aims. First, development of a site characterisation protocol and demonstration of its use to determine the principal geochemical characteristics of aquifer materials using batch experiments and to represent the information obtained in terms of a chemical model. The experimental results obtained for Ni²⁺ partitioning as a function of total Ni, pH, total Ca and total solid can be satisfactorily represented in terms of reactions with an ion exchange site and a single amphoteric surface hydroxyl site with ferrihydrite reaction constants. A second objective is the incorporation of the reactions in a coupled geochemistry/transport code, and to verify the applicability of the coupled code predictions for Ni²⁺ mass transfer by comparison with the results obtained during column tracer experiments. The breakthrough curve and equilibrium solid phase Ni loading, predicted by a one-dimensional coupled model for a column tracer experiment, agree closely with observed data.Additional studies are underway to reduce model conditionality, to extend the adsorption model to other analogue cations and anions, to incorporate the effect of natural organic matter and to take into consideration precipitation/dissolution of amorphous Fe surface phases.     

MULTIPLICATIVE,SEASONAL ARIMA MODELS FOR LAKE ERIE AND LAKE ONTARIO WATER LEVELS1

ABSTRACT: Federal agencies in the U.S. and Canada continuously examine methods to improve understanding and forecasting of Great Lakes water level dynamics in an effort to reduce the negative impacts of fluctuating levels incurred by interests using the lakes. The short term, seasonal and long term water level dynamics of lakes Erie and Ontario are discussed. Multiplicative, seasonal ARIMA models are developed for lakes Erie and Ontario using standardized, monthly mean level data for the period 1900 to 1986. The most appropriate model identified for each lake had the general form: (1 0 1)(0 1 1)₁₂. The data for each lake were subdivided by time periods (1900 to 1942;1 943 to 1986) and the model coefficients estimated for the subdivided data were similar, indicating general model stability for the entire period of record. The models estimated for the full data sets were used to forecast levels 1,2,3, and 6 months ahead for a period of high levels (1984 to 1986). The average absolute forecast error for Lake Erie was 0.049m, 0.076m, 0.091 m and 0.128m for the 1, 2,3, and 6 month forecasts, respectively. The average absolute forecast error for Lake Ontario was 0.058m, 0.095m, 0.120m and 0.136m for the 1,2,3, and 6 month forecasts, respectively. The ARIMA models provide additional information on water level time series structure and dynamics. The models also could be coordinated with current forecasting methods, possibly improving forecasting accuracy.     

Island biogeographical theory: Can it be used to predict lotic recovery rates?

Classic island biogeographic theory predicts that equilibrium will be reached when immigration and extinction rates are equal. These rates are modified by number of species in source area, number of intermediate islands, distance to recipient island, and size of intermediate islands. This general model has been variously modified and proposed to be a stochastic process with minimal competitive interaction or heavily deterministic. Predictive models of recovery (regardless of the end point chosen) have been based on the appropriateness of the MacArthur-Wilson models. Because disturbance frequency, severity, and intensity vary in their effect on community dynamics, we propose that disturbance levels should first be defined before evaluating the applicability of island biogeographical theory. Thus, we suggest a classification system of four disturbance levels based on recovery patterns by primary and secondary succession and faunal organization by primary (invasion of vacant areas) and secondary (remnant of previous community remains) processes. Level 1A disturbances completely destroy communities with no upstream or downstream sources of colonizers, while some component of near surface interstitial or hyporheic flora and fauna survive level 1B disturbances. Recovery has been reported to take from five years to longer than 25 years, when most invading colonists do not have an aerial form. Level 2 disturbances destroy the communities but leave upstream and downstream colonization sources (level 2A) and, sometimes, a hyporheic pool of colonizers (level 2B). Recovery studies have indicated primary succession and faunal structuring patterns (2A) with recovery times of 90–400 days or secondary succession and faunal structuring patterns (2B) with recovery times of 40–250 days. Level 3 disturbances result in reduction in species abundance and diversity along a stream reach; level 4 disturbances result in reduction of abundance and diversity in discrete patches. Both disturbance types lead to secondary succession and secondary faunal organization. Recovery rates can be quite rapid, varying from less than 10 days to 100 or more days. We suggest that island biogeographical models seem appropriate to recovery by secondary processes after level 3 and 4 disturbances, where competition may be an important organizing factor, while models of numerical abundance and resource tracking are probably of better use where community development is by primary succession (levels 1 and 2). Development of predictive recovery models requires research that addresses a number of fundamental questions. These include the role of hydrologic patterns on colonization dynamics, the role of nonaerial colonizers in recovery from level 1 disturbances, and assessment of the impact of changes in the order of invasion by colonizers of varying energetic efficiencies. Finally, we must be able to assemble these data and determine whether information that guides community organization at one level of disturbance can provide insights into colonization dynamics at other levels.     

Integrating forage,wildlife, water,and fish projections with timber projections at the regional level: A case study in southern United States

The impact of timber management and land-use change on forage production, turkey and deer abundance, red-cockaded woodpecker colonies, water yield, and trout abundance was projected as part of a policy study focusing on the southern United States. The multiresource modeling framework used in this study linked extant timber management and land-area policy models with newly developed models for forage, wildlife, fish, and water. Resource production was integrated through a commonly defined land base that could be geographically partitioned according to individual resource needs. Resources were responsive to changes in land use, particularly human-related, and timber management, particularly the harvest of older stands, and the conversion to planted pine.     

Community richness and stability in agricultural landscapes: The importance of surrounding habitats

In this paper, the role played by habitat diversity in the landscape on species richness and on the stability of farmland bird communities was investigated. Species richness was estimated on 374 samples monitored in farmland by the French breeding bird survey during the 2001–2005 period. A capture–recapture approach was used to estimate species richness accounting for the variation in detection probability among species of the 100 most common species detected in farmland. Landscape structure and composition were measured both in farmland and in adjacent habitats. The independent effect of each variable on community richness and stability was further assessed using hierarchical variance partitioning and taking spatial autocorrelation into account. A strong matrix effect was detected: non-cropped land deeply influenced richness and stability of bird assemblages.