An improved neutral landscape model for recreating real landscapes and generating landscape series for spatial ecological simulations

Many studies have assessed the effect of landscape patterns on spatial ecological processes by simulating these processes in computer‐generated landscapes with varying composition and configuration. To generate such landscapes, various neutral landscape models have been developed. However, the limited set of landscape‐level pattern variables included in these models is often inadequate to generate landscapes that reflect real landscapes. In order to achieve more flexibility and variability in the generated landscapes patterns, a more complete set of class‐ and patch‐level pattern variables should be implemented in these models. These enhancements have been implemented in Landscape Generator (LG), which is a software that uses optimization algorithms to generate landscapes that match user‐defined target values. Developed for participatory spatial planning at small scale, we enhanced the usability of LG and demonstrated how it can be used for larger scale ecological studies. First, we used LG to recreate landscape patterns from a real landscape (i.e., a mountainous region in Switzerland). Second, we generated landscape series with incrementally changing pattern variables, which could be used in ecological simulation studies. We found that LG was able to recreate landscape patterns that approximate those of real landscapes. Furthermore, we successfully generated landscape series that would not have been possible with traditional neutral landscape models. LG is a promising novel approach for generating neutral landscapes and enables testing of new hypotheses regarding the influence of landscape patterns on ecological processes. LG is freely available online.     

Simple temporal models for ecological systems with complex spatial patterns

Spatial patterns are ubiquitous in nature. Because these patterns modify the temporal dynamics and stability properties of population densities at a range of spatial scales, their effects must be incorporated in temporal ecological models that do not represent space explicitly. We demonstrate a connection between a simple parameterization of spatial effects and the geometry of clusters in an individual‐based predator–prey model that is both nonlinear and stochastic. Specifically we show that clusters exhibit a power‐law scaling of perimeter to area with an exponent close to unity. In systems with a high degree of patchiness, similar power‐law scalings can provide a basis for applying simple temporal models that assume well‐mixed conditions.     

Choosing appropriate temporal and spatial scales for ecological restoration

Classic ecological restoration seems tacitly to have taken the Clementsian “balance of nature” paradigm for granted: plant succession terminates in a climax community which remains at equilibrium until exogenously disturbed after which the process of succession is restarted until the climax is reached. Human disturbance is regarded as unnatural and to have commenced in the Western Hemisphere at the time of European incursion. Classic ecological restoration thus has a clear and unambiguous target and may be conceived as aiming to foreshorten the natural processes that would eventually lead to the climax of a given site, which may be determined by its state at “settlement”. According to the new “flux of nature” paradigm in ecology a given site has notelos and is constantly changing. Human disturbance is ubiquitous and long-standing, and at certain spatial and temporal scales is “incorporated”. Any moment in the past 10,000 years that may be selected as a benchmark for restoration efforts thus appears to be arbitrary. Two prominent conservationists have therefore suggested that the ecological conditions in North America at the Pleistocene—Holocene boundary, prior to the anthropogenic extinction of the Pleistocene megafauna, be the target for ecological restoration. That suggestion explicitly assumes evolutionary temporal scales and continental spatial scales as the appropriate frame of reference for ecological restoration. However, ecological restoration should be framed in ecological spatio-temporal scales, which may be defined temporally in reference to ecological processes such as disturbance regimes and spatially in reference to ecological units such as landscapes, ecosystems, and biological provinces. Ecological spatio-temporal scales are also useful in achieving a scientifically defensible distinction between native and exotic species, which plays so central a role in the practice of ecological restoration and the conservation of biodiversity. Because post-settlement human disturbances have exceeded the limits of such scales, settlement conditions can be justified scientifically as appropriate targets of restoration efforts without recourse to obsolete teleological concepts of equilibria and without ignoring the presence and ecological influence of indigenous peoples.     

基于NDVI时间序列轨迹的草原露天矿区植被时空动态特征

采矿作用下草原露天矿区植被时空动态特征尚不明确.以胜利露天矿区为例,选取MODIS和Landsat影像,利用ESTARFM构建2001—2015年植被生长期一致的年际Landsat时间序列,以时间分割算法拟合像元NDVI时间序列轨迹,结合轨迹形态特征提取早期扰动型、持续扰动型、扰动稳定型、扰动稳定恢复型和扰动恢复型5种植被动态类型及各类型的时间特征.结果表明:胜利矿区植被动态类型以扰动恢复型为主,占各类型像元总数的55.2%,扰动稳定型和持续下降型次之,分别为25.6%和11.0%,早期扰动型和扰动稳定恢复型较小,分别为3.5%和4.7%.扰动多发于2004—2009年,稳定期多始于2008年,空间上多分布于露天采场和排土场,恢复期多始于2008年和2010年,其空间范围较小且集中于矿井外围和排土场.扰动持续时长以1年为主,稳定期持续时长以7年为主,恢复期持续时长中稳定恢复型为2～5年,扰动恢复型为8年.     

景观生态界面边界判定与动态模拟研究进展

栖息地边界对景观结构和功能具深远影响,既影响局部又作用于更大尺度区域的生态过程,同时界面的动态特征通过反馈机制影响着不同种群、群落以及生态系统。因此,在景观生态界面研究中,界面尺度依赖性和时空动态性的定量化研究已成为模型和统计学者的研究热点。鉴于此,通过介绍生态界面描述、界面监测及相关边界动态变化特征研究,阐述统计学和数学方法在不同生态系统、生态过程及尺度下界面研究中的应用,同时指出两者结合研究在生态界面定量的研究中仍面临着概念和方法上的挑战,为进一步提高景观生态界面综合研究水平提供参考。     

Occupancy versus colonization–extinction models for projecting population trends at different spatial scales

Understanding spatiotemporal population trends and their drivers is a key aim in population ecology. We further need to be able to predict how the dynamics and sizes of populations are affected in the long term by changing landscapes and climate. However, predictions of future population trends are sensitive to a range of modeling assumptions. Deadwood‐dependent fungi are an excellent system for testing the performance of different predictive models of sessile species as these species have different rarity and spatial population dynamics, the populations are structured at different spatial scales, and they utilize distinct substrates. We tested how the projected large‐scale occupancies of species with differing landscape‐scale occupancies are affected over the coming century by different modeling assumptions. We compared projections based on occupancy models against colonization–extinction models, conducting the modeling at alternative spatial scales and using fine‐ or coarse‐resolution deadwood data. We also tested effects of key explanatory variables on species occurrence and colonization–extinction dynamics. The hierarchical Bayesian models applied were fitted to an extensive repeated survey of deadwood and fungi at 174 patches. We projected higher occurrence probabilities and more positive trends using the occupancy models compared to the colonization–extinction models, with greater difference for the species with lower occupancy, colonization rate, and colonization:extinction ratio than for the species with higher estimates of these statistics. The magnitude of future increase in occupancy depended strongly on the spatial modeling scale and resource resolution. We encourage using colonization–extinction models over occupancy models, modeling the process at the finest resource‐unit resolution that is utilizable by the species, and conducting projections for the same spatial scale and resource resolution at which the model fitting is conducted. Further, the models applied should include key variables driving the metapopulation dynamics, such as the availability of suitable resource units, habitat quality, and spatial connectivity.     

森林多目标经营单元的时空尺度扩展

森林多目标经营是我国林业发展的重要趋势,而森林本身的特性以及国内外的众多研究证明,森林多目标经营需要考虑多个时空尺度,以满足其生态、经济和社会效益的综合发挥。在界定多功能林业和森林多目标经营的概念及基础上,系统地分析了传统森林经营时空尺度和现代森林多目标经营时空尺度的联系与区别,提出现代森林多目标经营单元空间尺度上应该满足区域、景观和林分多尺度的需要,而在时间尺度上则要综合考虑短期、中期和长期,并以露水河林业局为例给出具体的研究思路,以期为我国开展森林多目标经营提供借鉴。     

Estimating and controlling for spatial structure in the study of ecological communities

Aim Variation partitioning based on canonical analysis is the most commonly used analysis to investigate community patterns according to environmental and spatial predictors. Ecologists use this method in order to understand the pure contribution of the environment independent of space, and vice versa, as well as to control for inflated type I error in assessing the environmental component under spatial autocorrelation. Our goal is to use numerical simulations to compare how different spatial predictors and model selection procedures perform in assessing the importance of the spatial component and in controlling for type I error while testing environmental predictors. Innovation We determine for the first time how the ability of commonly used (polynomial regressors) and novel methods based on eigenvector maps compare in the realm of spatial variation partitioning. We introduce a novel forward selection procedure to select spatial regressors for community analysis. Finally, we point out a number of issues that have not been previously considered about the joint explained variation between environment and space, which should be taken into account when reporting and testing the unique contributions of environment and space in patterning ecological communities. Main conclusions In tests of species‐environment relationships, spatial autocorrelation is known to inflate the level of type I error and make the tests of significance invalid. First, one must determine if the spatial component is significant using all spatial predictors (Moran's eigenvector maps). If it is, consider a model selection for the set of spatial predictors (an individual‐species forward selection procedure is to be preferred) and use the environmental and selected spatial predictors in a partial regression or partial canonical analysis scheme. This is an effective way of controlling for type I error in such tests. Polynomial regressors do not provide tests with a correct level of type I error.     

Time series analysis models for estimation of greenhouse gas emitted by different sectors in Turkey

Due to the increasing global warming in the world, analyzing greenhouse gas emissions is a crucial issue. This study has examined greenhouse gas emissions in Turkey according to energy sector, industrial processes sector, agriculture sector and waste sector. Then, time series analysis models are used to estimate greenhouse gas emissions based on sectors. Models' performances are tested using mean error, mean absolute error and root mean square error. The results show that forecasting models have a good potential to estimate the national greenhouse gas emissions for different sector within a reasonable error. The study results will help organize and estimate the national greenhouse gas emissions inventory.     

Effects of environmental synchrony and density-dependent dispersal on temporal and spatial slopes of Taylor's law

Taylor's law (TL) is an empirical rule that describes an approximate relationship between the variance and mean of population density: log₁₀(variance) ≈ log₁₀(a) + b × log₁₀(mean). Population synchrony is another prevailing feature observed in empirical populations. This study investigated the effects of environmental synchrony and density-dependent dispersal on the temporal (b_T) and spatial (b_S) slopes of TL, using an empirical dataset of gray-sided vole populations and simulation analyses based on the second-order autoregressive (AR) model. Eighty-five empirical populations satisfied the temporal and spatial TLs with b_T = 1.943 (±SE 0.143) and b_S = 1.579 (±SE 0.136). The pairwise synchrony of population was 0.377 ± 0.199 (mean ± SD). Most simulated populations that obeyed the AR model satisfied the form of the temporal and spatial TLs without being affected by the environmental synchrony and density-dependent dispersal; however, those simulated slopes were too steep. The incorporation of environmental synchrony resulted in reduced simulated slopes, but those slopes, too, were still unrealistically steep. By incorporating density-dependent dispersal, simulated slopes decreased and fell within a realistic range. However, the simulated populations without environmental synchrony did not exhibit an adequate degree of density synchrony. In simulations that included both environmental synchrony and density-dependent dispersal, 92.7% of the simulated datasets provided realistic values for b_T, b_S and population synchrony. Because the two slopes were more sensitive to the variation of density-dependent dispersal than that of environmental synchrony, density-dependent dispersal may be the key to the determination of b_T and b_S.     

Remote sensing and geographic information systems in the spatial temporal dynamics modeling of infectious diseases

Similar to species immigration or exotic species invasion, infectious disease transmission is strengthened due to the globalization of human activities. Using schistosomiasis as an example, we propose a conceptual model simulating the spatio-temporal dynamics of infectious diseases. We base the model on the knowledge of the interrelationship among the source, media, and the hosts of the disease. With the endemics data of schistosomiasis in Xichang, China, we demonstrate that the conceptual model is feasible; we introduce how remote sensing and geographic information systems techniques can be used in support of spatio-temporal modeling; we compare the different effects caused to the entire population when selecting different groups of people for schistosomiasis control. Our work illustrates the importance of such a modeling tool in supporting spatial decisions. Our modeling method can be directly applied to such infectious diseases as the plague, lyme disease, and hemorrhagic fever with renal syndrome. The application of remote sensing and geographic information systems can shed light on the modeling of other infectious disease and invasive species studies.     

Remote sensing and geographic information systems in the spatial temporal dynamics modeling of infectious diseases

Similar to species immigration or exotic species invasion,infectious disease transmis-sion is strengthened due to the globalization of human activities. Using schistosomiasis as an exam-ple,we propose a conceptual model simulating the spatio-temporal dynamics of infectious diseases. We base the model on the knowledge of the interrelationship among the source,media,and the hosts of the disease. With the endemics data of schistosomiasis in Xichang,China,we demonstrate that the conceptual model is feasible; we introduce how remote sensing and geographic information systems techniques can be used in support of spatio-temporal modeling; we compare the different effects caused to the entire population when selecting different groups of people for schistosomiasis control. Our work illustrates the importance of such a modeling tool in supporting spatial decisions. Our mod-eling method can be directly applied to such infectious diseases as the plague,lyme disease,and hemorrhagic fever with renal syndrome. The application of remote sensing and geographic informa-tion systems can shed light on the modeling of other infectious disease and invasive species studies.     

基于生态系统服务功能和生态敏感性的自然生态空间管制分区研究

自然生态空间分区管制是国土空间管制的重要组成部分,也是国内外生态环境研究的热点。针对国内自然生态空间用途管制暂处于试点阶段,有关管制的方法有待进一步探究的情况下,旨在通过江西省自然生态空间管制分区,为实现合理保护自然生态资源,促进自然生态系统健康有序发展提供参考。以江西省为研究对象,借助GIS空间分析技术,通过生态系统服务功能重要性和生态敏感性评价,构建二维关联判断矩阵,进行自然生态空间管制分区,并以此提出相关的管制建议。结果表明:江西省自然生态空间总面积为117924.67 km²,约占全省总面积的70.66%,从空间上可划分为高重要高敏感区、中度重要敏感区和低重要低敏感区3种类型区;其中,高重要高敏感区以生态保护和生态修复为主,实施严格的区域准入措施;中度重要敏感区可依托区域生态资源优势,合理开展以维护、改善生态系统服务功能为主要目的生态经营活动;低重要低敏感区允许在不破坏生态系统结构和功能的前提下,适度开展一定规模的生产建设活动,减少污染排放,增强区域生态功能。全省16个国家自然保护区基本位于高重要高敏感范围内。基于生态系统服务功能与生态敏感性的自然生态空间...     

Pollen availability for predaceous mites on apple: spatial and temporal heterogeneity

It has been suggested that an abundance of alternate food early in the spring may be critical to the ability of generalist predaceous mites to suppress spider mite pests. One alternate food that is typically very abundant in spring is wind-dispersed pollen. Here we investigate, at several spatial scales, the heterogeneity in the availability of pollen to predaceous mites on apple. We found pollen to be abundant on apple leaves very soon after they opened (>100 grains/cm 2 ), and that the dominant pollen types at this time were wind-dispersed tree pollens (Betulaceae and Pinaceae). We found that most of the spatial variation in pollen abundance occurred at either small spatial scales (within trees) or very large spatial scales (among orchard blocks). Variability among orchards was clearly influenced by the surrounding vegetation, and probably also by the management regime (frequency of mowing). Spatial heterogeneity in pollen availability may affect the build-up of predatory mite populations in the spring, as we found early season abundances of Typhlodromus pyri (Phytoseiidae) and Zetzellia mali (Stigmaeidae) to be better correlated with early season pollen density than with abundance of mite prey (Aculus schlechtendali).     

Past states of continuous-time Markov models for ecological communities

Discrete-time Markov chains are often used to model communities of sessile organisms. The community is described by a set of discrete states, which may represent species or groups of species. Transitions between states are modelled using a stochastic matrix. A recent study showed how the time-reversal of such a Markov chain can be used to estimate the distribution of time since the last occurrence of some state of interest (such as empty space) at a point, given the current state of the point. However, if the underlying process operates in continuous time but is observed at regular intervals, this distribution describes the time since the last possible observation of the state of interest, rather than the time since its last occurrence. We show how to obtain the distribution of time since the last occurrence of a state of interest for a continuous-time homogeneous Markov chain. The expected time since the last occurrence of an initial state can be interpreted as a measure of the successional rank of a state. We show how to distinguish between different ways in which a state can have high successional rank. We apply our results to a marine subtidal community.     

Symbolic dynamics for identifying similarity between rhythms of ecological time series

Explaining the associations between animal populations or between population and environmental signals is an important challenge. The time series that quantify animal populations are often complex, nonlinear, noisy and non‐stationary. These characteristics may make it inappropriate to use traditional techniques when analysing these time series and their mutual dependencies. Here I propose to use symbolic dynamics and techniques from Information Theory to evaluate the degree of dynamic cohesion between time series fluctuations. The main idea is to check whether two (or more) signals tend to oscillate simultaneously, rising and falling together with the same rhythm. Based on synthetic and real time series, I demonstrate that this method is robust to the presence of noise and to the short length of the analysed time series and gives relevant information about the weak relationships between different series. Furthermore, this method appears as simple as classical cross‐correlation and outperforms it in the analysed examples.     

基于小波变换的卧龙国家级自然保护区植被时空变化分析

提出一种基于小波变换从长时间序列、大范围遥感数据中快速、自动化检测植被动态变化的方法。以MODIS 500m空间分辨率,16d合成的NDVI数据为数据源,对受到2008年5月12日汶川地震严重影响的卧龙国家级自然保护区内2003年至2012年的植被动态变化进行时空分析,为保护生态多样性及生态系统的稳定性提供依据。研究表明:1)地震后保护区内植被指数减少的面积大范围增加,且波动较震前更为明显,统计分析结果能够更为直观地反映地震及其次生灾害等极端现象对该地区植被的破坏程度;2)保护区内植被指数极值变化多发生在夏季或秋季,较低海拔地区极值变化多发生在夏季,而在高海拔地区则多发生在秋季;3)在大熊猫最适宜栖息的区域(2600—2800m)植被指数极值减少量大于0.4的范围大于增加量大于0.4的范围,反映出植被在震后的恢复状况并没达到理想的水平。同时发现在该海拔区域范围内植被指数减少的面积在春夏两季较大,表明在该时间段卧龙地区大熊猫最适宜生存区域的植被情况较为不稳定,需更为关注其动态,采取适当的保护措施。