Factors promoting or inhibiting Turing instability in spatially extended prey-predator systems

The emergence of inhomogeneities in the distributions of the abundances of spatially extended prey-predator systems is investigated. The method of analysis, based on the notion of diffusive (Turing) instability, is systematically applied to nine different models obtained by introducing an extra-factor into the standard Rosenzweig-MacArthur prey-predator model. The analysis confirms that the standard model is critical in the context of Turing instability, and that the introduction of any small amount of the extra-factor can easily promote or inhibit the emergence of spatial patterns.     

A Perspective on Nonstationarity and Water Management1

Hirsch, Robert M., 2011. A Perspective on Nonstationarity and Water Management. Journal of the American Water Resources Association (JAWRA) 47(3):436‐446. DOI: 10.1111/j.1752‐1688.2011.00539.x Abstract: This essay offers some perspectives on climate‐related nonstationarity and water resources. Hydrologists must not lose sight of the many sources of nonstationarity, recognizing that many of them may be of much greater magnitude than those that may arise from climate change. It is paradoxical that statistical and deterministic approaches give us better insights about changes in mean conditions than about the tails of probability distributions, and yet the tails are very important to water management. Another paradox is that it is difficult to distinguish between long‐term hydrologic persistence and trend. Using very long hydrologic records is helpful in mitigating this problem, but does not guarantee success. Empirical approaches, using long‐term hydrologic records, should be an important part of the portfolio of research being applied to understand the hydrologic response to climate change. An example presented here shows very mixed results for trends in the size of the annual floods, with some strong clusters of positive trends and a strong cluster of negative trends. The potential for nonstationarity highlights the importance of the continuity of hydrologic records, the need for repeated analysis of the data as the time series grow, and the need for a well‐trained cadre of scientists and engineers, ready to interpret the data and use those analyses to help adjust the management of our water resources.     

Robustness and vulnerability of community irrigation systems: The case of the Taos valley acequias

Traditional economic and policy analysis theory has emphasized the implementation of private or public property rights regimes in order to sustainably manage natural resources. More recent work has challenged this approach by examining the strengths and weakness of common property governance of such resources. This paper contributes to this literature by analyzing the acequia irrigation communities in northern New Mexico. Through statistical analysis, we find that the acequias’ ability to maintain collective-action as estimated by a critical performance function, crop production, is aided by water sharing agreements and access to groundwater, and that it is hampered by property rights fragmentation and urbanization.     

The landscape epidemiology of foot-and-mouth disease in South Africa: A spatially explicit multi-agent simulation

This study aimed at understanding how landscape heterogeneity influences outbreaks of contagious diseases in southern Africa. Landscape attributes influence patterns of movement and behaviour of animal hosts, virus spread and survival, as well as land use practices. A multi-agent simulation was developed to represent the spatial and temporal dynamics of pathogens between human-livestock and wildlife interfaces at the fringe of large wildlife conservation areas. The model represents the three main elements associated with epidemics - populations, space, and time - to simulate direct contacts between wildlife and livestock. The dynamics of these populations emerge from interactions between agents and the landscape. The model was calibrated to represent the transmission of foot-and-mouth disease through direct contact at the border of the Kruger National Park in South Africa. In the region, African buffaloes (Syncerus caffer) act as reservoirs of the virus and spread the infection to domestic cattle bordering the park. We tested the sensitivity of various factors influencing contact rate between buffaloes and cattle, and thus the risk of foot-and-mouth disease transmission. Results show that cattle-buffalo contacts mostly depend on the range of displacements of cattle and buffaloes, as influenced by the landscape configuration, and on the number of fence breakages multiplied by the time between breakage and repair. Contacts take place not only close to water-points but also in grazing areas, within an area up to 6 km from the fence.     

Forecasting population trend from the scaling pattern of occupancy

Forecasting the temporal trend of a focal species, its range expansion or retraction, provides crucial information regarding population viability. To this end, we require the accumulation of temporal records which is evidently time consuming. Progress in spatial data capturing has enabled rapid and accurate assessment of species distribution across large scales. Therefore, it would be appealing to infer the temporal trends of populations from the spatial structure of their distributions. Based on a combination of models from the fields of range dynamics, occupancy scaling and spatial autocorrelation, here I present a model for forecasting the population trend solely from its spatial distribution. Numerical tests using cellular automata confirm a positive correlation, as inferred from the model, between the temporal change in species range sizes and the exponent of the power-law scaling pattern of occupancy. The model is thus recommended for rapid estimation of species range dynamics from a single snapshot of its current distribution. Further applications in biodiversity conservation could provide a swift risk assessment, especially, for endangered and invasive species.     

Predicting modes of spatial change from state-and-transition models

State-and-transition models (STMs) can represent many different types of landscape change, from simple gradient-driven transitions to complex, (pseudo-) random patterns. While previous applications of STMs have focused on individual states and transitions, this study addresses broader-scale modes of spatial change based on the entire network of states and transitions. STMs are treated as mathematical graphs, and several metrics from algebraic graph theory are applied—spectral radius, algebraic connectivity, and the S-metric. These indicate, respectively, the amplification of environmental change by state transitions, the relative rate of propagation of state changes through the landscape, and the degree of system structural constraints on the spatial propagation of state transitions. The analysis is illustrated by application to the Gualalupe/San Antonio River delta, Texas, with soil types as representations of system states. Concepts of change in deltaic environments are typically based on successional patterns in response to forcings such as sea level change or river inflows. However, results indicate more complex modes of change associated with amplification of changes in system states, relatively rapid spatial propagation of state transitions, and some structural constraints within the system. The implications are that complex, spatially variable state transitions are likely, constrained by local (within-delta) environmental gradients and initial conditions. As in most applications, the STM used in this study is a representation of observed state transitions. While the usual predictive application of STMs is identification of local state changes associated with, e.g., management strategies, the methods presented here show how STMs can be used at a broader scale to identify landscape scale modes of spatial change.     

Simulation and analysis of outbreaks of bark beetle infestations and their management at the stand level

Outbreaks of bark beetles in forests can result in substantial economic losses. Understanding the factors that influence the development and spread of bark beetle outbreaks is crucial for forest management and for predicting outbreak risks, especially with the expected global warming. Although much research has been done on the ecology and phenology of bark beetles, the complex interplay between beetles, host trees, beetle antagonists and forest management makes predicting beetle population development especially difficult. Using the recent infestations of the European Spruce Bark Beetle (Ips typographus L. Col. Scol.) in the Bavarian Forest National Park (Germany) as a case study, we developed a spatially explicit agent-based simulation model (SAMBIA) that takes into account individual trees and beetles. This model primarily provides a tool for analysing and understanding the spatial and temporal aspects of bark beetles outbreaks at the stand scale. Furthermore, the model should allow an estimation of the effectiveness of concurrent impacts of both antagonists and management to confine outbreak dynamics in practice. We also used the model to predict outbreak probabilities in various settings. The simulation results indicated a distinct threshold behaviour of the system in response to pressure by antagonists or management of the bark beetle population. Despite the different scenarios considered, we were able to extract from the simulations a simple rule of thumb for the successful control of an outbreak: if roughly 80% of individual beetles are killed by antagonists or foresters, outbreaks will rarely take place. Our model allows the core dynamics of this complex system to be reduced to this inherent common denominator.     

Estimating the vulnerability of fifteen tree species under changing climate in Northwest North America

In the Pacific northwestern (PNW) region of North America, climatic conditions have significantly warmed since a predominantly cool phase of the Pacific North American circulation patterns between 1950 and 1975. What are the implications of this shift in climate for the vulnerability of native tree species? To address this question, we combined mechanistic and statistical models to assess where a variety of native tree species might be more vulnerable within their recorded ranges and where they might potentially migrate. For long-lived species that are well adapted to compete, seasonal differences in photosynthesis and water use offer insights helpful in predicting their distributions. To evaluate the general response of conifers to climatic variation across the region, we previously applied a process-based model (3-PG), to simulate the growth and maximum leaf area index that Douglas-fir could attain within recognized forested areas. We then constructed automated decision tree models to define and map the ecological distributions of 15 tree species based on differences in how photosynthesis was constrained by drought, daytime temperatures, high evaporative demand, and the frequency of frost. For the baseline climate period (1950-1975), the decision tree models predicted presence and absence of each species at ∼23,000 observations with an average accuracy of 81%, with an average kappa statistic of 0.74. In this paper the same models were run annually for the period between 1976 and 2006 for each species, and the areas defined as remaining suitable or becoming vulnerable to disturbance were identified based on whether more or less than half of the years fell within the originally defined limits. Based on these criteria, 70% of the species recorded ranges remained suitable, with 30% deemed vulnerable. Results varied notably by species with western red cedar and western hemlock remaining highly adapted, with potential for range expansion in area of up to 50% relative to the baseline period. In contrast, ponderosa pine, lodgepole pine, grand, and noble fir were classified as vulnerable with potential net contractions in their ranges. The analysis was extended through the rest of the 21st century using climatic projections from the Canadian global circulation model with a high fossil fuel emission scenario (A2) and compared to other previously published species range predictions.     

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。