Plant species composition shifts in the Tatra Mts as a response to environmental change: a resurvey study after 90 years

Mountain vegetation is often considered highly sensitive to climate and land-use changes due to steep environmental gradients determining local plant species composition. In this study we present plant species compositional shifts in the Tatra Mts over the past 90 years and discuss the potential drivers of the changes observed. Using historical vegetation studies of the region from 1927, we resurveyed 76 vegetation plots, recording the vascular flora of each plot using the same methodology as in the original survey. We used an indirect method to quantify plant species compositional shifts and to indicate which environmental gradients could be responsible for these shifts: by calculating shifts in estimated species optima as reflected in shifts in the ecological indicator values of co-occurring species. To find shifts in species composition, focusing on each vegetation type separately, we used ordination (DCA). The species optimum changed significantly for at least one of the tested environmental gradients for 26 of the 95 plant species tested; most of these species changed in terms of the moisture indicator value. We found that the strongest shifts in species composition were in mylonite grassland, snowbed and hygrophilous tall herb communities. Changes in precipitation and increase in temperature were found to most likely drive compositional shifts in vegetation resurveyed. It is likely that the combined effect of climate change and cessation of sheep grazing has driven a species composition shift in granite grasslands communities.     

Empirical orthogonal function regression: Linking population biology to spatial varying environmental conditions using climate projections

Ecologists and oceanographers inform population and ecosystem management by identifying the physical drivers of ecological dynamics. However, different research communities use different analytical tools where, for example, physical oceanographers often apply rank‐reduction techniques (a.k.a. empirical orthogonal functions [EOF]) to identify indicators that represent dominant modes of physical variability, whereas population ecologists use dynamical models that incorporate physical indicators as covariates. Simultaneously modeling physical and biological processes would have several benefits, including improved communication across sub‐fields; more efficient use of limited data; and the ability to compare importance of physical and biological drivers for population dynamics. Here, we develop a new statistical technique, EOF regression, which jointly models population‐scale dynamics and spatially distributed physical dynamics. EOF regression is fitted using maximum‐likelihood techniques and applies a generalized EOF analysis to environmental measurements, estimates one or more time series representing modes of environmental variability, and simultaneously estimates the association of this time series with biological measurements. By doing so, it identifies a spatial map of environmental conditions that are best correlated with annual variability in the biological process. We demonstrate this method using a linear (Ricker) model for early‐life survival (“recruitment”) of three groundfish species in the eastern Bering Sea from 1982 to 2016, combined with measurements and end‐of‐century projections for bottom and sea surface temperature. Results suggest that (a) we can forecast biological dynamics while applying delta‐correction and statistical downscaling to calibrate measurements and projected physical variables, (b) physical drivers are statistically significant for Pacific cod and walleye pollock recruitment, (c) separately analyzing physical and biological variables fails to identify the significant association for walleye pollock, and (d) cod and pollock will likely have reduced recruitment given forecasted temperatures over future decades.     

Using species‐environmental amplitudes to predict pH values from vegetation

Question: Species optima or indicator values are frequently used to predict environmental variables from species composition. The present study focuses on the question whether predictions can be improved by using species environmental amplitudes instead of single values representing species optima. Location: Semi‐natural, deciduous hardwood forests of northwestern Germany. Methods: Based on a data set of 558 relevés, species responses (presence/absence) to pH were modelled with Huisman‐Olff‐Fresco (HOF) regression models. Species amplitudes were derived from response curves using three different methods. To predict the pH from vegetation, a maximum amplitude overlap method was applied. For comparison, predictions resulting from several established methods, i. e. maximum likelihood/present and absent species, maximum likelihood/present species only, mean weighted averages and mean Ellenberg indicator values were calculated. The predictive success (squared Pearson's r and root mean square error of prediction) was evaluated using an independent data set of 151 relevés. Results: Predictions based upon amplitudes defined by maximum Cohen's x probability threshold yield the best results of all amplitude definitions (R²= 0.75, RMSEP = 0.52). Provided there is an even distribution of the environmental variable, amplitudes defined by predicted probability exceeding prevalence are also suitable (R²= 0.76, RMSEP = 0.55). The prediction success is comparable to maximum likelihood (present species only) and – after rescaling – to mean weighted averages. Predicted values show a good linearity to observed pH values as opposed to a curvilinear relationship of mean Ellenberg indicator values. Transformation or rescaling of the predicted values is not required. Conclusions: Species amplitudes given by a minimum and maximum boundary for each species can be used to efficiently predict environmental variables from species composition. The predictive success is superior to mean Ellenberg indicator values and comparable to mean indicator values based on species weighted averages.     

A new biological indicator to assess the ecological status of Mediterranean trout type streams

Fuelled by the generalized degradation of freshwater ecosystems, the development of tools to assess their ecological status has been the focus of intensive research in the last decades. Although fish are one of the key biological quality elements used to describe the ecological status of rivers, fish metrics that accurately respond to disturbances in Mediterranean trout type streams are still lacking. In these systems, multimetric indices are not optimal indicators because of their low species richness and abundances, thus alternative approaches are needed. Since carrying capacity defines the potential maximum abundance of fish that can be sustained by a river, its relationship with actual density (D/K ratio) could be an accurate indicator of population conservation status and consequently of the ecological status of the river. Based on this rationale, we modeled carrying capacity dynamics for 37 brown trout populations during a 12-year study period. We analyzed the response of the D/K ratio to a gradient of increasing environmental harshness and degradation in order to assess its suitability to accurately measure brown trout conservation status. Our results showed that the D/K ratio was highly sensitive to temporal and spatial variations in environmental conditions and the levels of human-induced environmental degradation. Variations in the environmental and human degradation factors included in the best explaining regression models developed for the whole population and by age classes accounted for between 58 and 81% of the variation in the D/K ratio. Likewise, the D/K ratio was sensitive to both general and life stage specific disturbance factors. Further analyses helped identify the factors limiting population abundance. Therefore, the D/K ratio could be an interesting indicator to consider when defining objective management plans and corrective actions in degraded rivers and streams subject to Mediterranean climatic conditions.     

Quantifying Patterns of Change in Marine Ecosystem Response to Multiple Pressures

The ability to understand and ultimately predict ecosystem response to multiple pressures is paramount to successfully implement ecosystem-based management. Thresholds shifts and nonlinear patterns in ecosystem responses can be used to determine reference points that identify levels of a pressure that may drastically alter ecosystem status, which can inform management action. However, quantifying ecosystem reference points has proven elusive due in large part to the multi-dimensional nature of both ecosystem pressures and ecosystem responses. We used ecological indicators, synthetic measures of ecosystem status and functioning, to enumerate important ecosystem attributes and to reduce the complexity of the Northeast Shelf Large Marine Ecosystem (NES LME). Random forests were used to quantify the importance of four environmental and four anthropogenic pressure variables to the value of ecological indicators, and to quantify shifts in aggregate ecological indicator response along pressure gradients. Anthropogenic pressure variables were critical defining features and were able to predict an average of 8-13% (up to 25-66% for individual ecological indicators) of the variation in ecological indicator values, whereas environmental pressures were able to predict an average of 1-5 % (up to 9-26% for individual ecological indicators) of ecological indicator variation. Each pressure variable predicted a different suite of ecological indicator’s variation and the shapes of ecological indicator responses along pressure gradients were generally nonlinear. Threshold shifts in ecosystem response to exploitation, the most important pressure variable, occurred when commercial landings were 20 and 60% of total surveyed biomass. Although present, threshold shifts in ecosystem response to environmental pressures were much less important, which suggests that anthropogenic pressures have significantly altered the ecosystem structure and functioning of the NES LME. Gradient response curves provide ecologically informed transformations of pressure variables to explain patterns of ecosystem structure and functioning. By concurrently identifying thresholds for a suite of ecological indicator responses to multiple pressures, we demonstrate that ecosystem reference points can be evaluated and used to support ecosystem-based management.     

莫莫格湿地芦苇对水盐变化的生理生态响应

认识湿地植物对不同水盐环境的生理生态响应特征和规律,是确定湿地生态需水阈值的关键,为湿地生态需水量计算及生态恢复提供科学依据.通过对莫莫格湿地水盐环境因子与芦苇生理生态特征指标进行调查研究,并利用国际通用植被数量分析软件CANOCO4.5对其关系进行了冗余度分析(RDA).结果表明:湿地水深、Na+,HCO(3)含量3个环境因子组合对芦苇生理生态特征变异的解释量达到54.7％,说明这3个变量是影响芦苇生理生态特征变异的重要因子,水深是关键驱动因子.水深与芦苇株高、生物量、叶绿素含量、最大光化学效率Fv/FM以及光化学性能指数PIABS成显著正相关,随着水深的增加,芦苇株高、生物量以及叶绿素含量等逐渐增加.Na+含量、HCO(3)含量与芦苇生理生态特征的相关性没有达到显著水平.因此,中轻度盐碱湿地生态恢复需要重点考虑水深条件对湿地生态的影响,其次是水质( Na+/HCO(3))因素的影响作用,确保适宜生态水位,满足生态恢复需要.     

Early Warning Signs in Social-Ecological Networks

A number of social-ecological systems exhibit complex behaviour associated with nonlinearities, bifurcations, and interaction with stochastic drivers. These systems are often prone to abrupt and unexpected instabilities and state shifts that emerge as a discontinuous response to gradual changes in environmental drivers. Predicting such behaviours is crucial to the prevention of or preparation for unwanted regime shifts. Recent research in ecology has investigated early warning signs that anticipate the divergence of univariate ecosystem dynamics from a stable attractor. To date, leading indicators of instability in systems with multiple interacting components have remained poorly investigated. This is a major limitation in the understanding of the dynamics of complex social-ecological networks. Here, we develop a theoretical framework to demonstrate that rising variance—measured, for example, by the maximum element of the covariance matrix of the network—is an effective leading indicator of network instability. We show that its reliability and robustness depend more on the sign of the interactions within the network than the network structure or noise intensity. Mutualistic, scale free and small world networks are less stable than their antagonistic or random counterparts but their instability is more reliably predicted by this leading indicator. These results provide new advances in multidimensional early warning analysis and offer a framework to evaluate the resilience of social-ecological networks.     

Multiple facets of stream macroinvertebrate alpha diversity are driven by different ecological factors across an extensive altitudinal gradient

Environmental filtering and spatial structuring are important ecological processes for the generation and maintenance of biodiversity. However, the relative importance of these ecological drivers for multiple facets of diversity is still poorly understood in highland streams. Here, we examined the responses of three facets of stream macroinvertebrate alpha diversity to local environmental, landscape‐climate and spatial factors in a near‐pristine highland riverine ecosystem. Taxonomic (species richness, Shannon diversity, and evenness), functional (functional richness, evenness, divergence, and Rao's Quadratic entropy), and a proxy of phylogenetic alpha diversity (taxonomic distinctness and variation in taxonomic distinctness) were calculated for macroinvertebrate assemblages in 55 stream sites. Then Pearson correlation coefficient was used to explore congruence of indices within and across the three diversity facets. Finally, multiple linear regression models and variation partitioning were employed to identify the relative importance of different ecological drivers of biodiversity. We found most correlations between the diversity indices within the same facet, and between functional richness and species richness were relatively strong. The two phylogenetic diversity indices were quite independent from taxonomic diversity but correlated with functional diversity indices to some extent. Taxonomic and functional diversity were more strongly determined by environmental variables, while phylogenetic diversity was better explained by spatial factors. In terms of environmental variables, habitat‐scale variables describing habitat complexity and water physical features played the primary role in determining the diversity patterns of all three facets, whereas landscape factors appeared less influential. Our findings indicated that both environmental and spatial factors are important ecological drivers for biodiversity patterns of macroinvertebrates in Tibetan streams, although their relative importance was contingent on different facets of diversity. Such findings verified the complementary roles of taxonomic, functional and phylogenetic diversity, and highlighted the importance of comprehensively considering multiple ecological drivers for different facets of diversity in biodiversity assessment.     

Do we really need phytosociological classes to calibrate Ellenberg indicator values?

Abstract. Wamelink et al. (2002) calibrated Ellenberg indicator values for acidity and water availability against measured soil pH and measured mean spring groundwater level (MSL), respectively. Linear regression between indicator value and measured value of all the observations gave a poor fit. Regression lines per phytosociological vegetation class, on the other hand, generally described the observations well. In this article we demonstrate that this result is, at least partly, an artefact. First, because the data utilized are likely to contain systematic errors, and second, because a wrong regression model was applied. A sigmoid function for the relation between the indicator value for water availability and MSL gives a far better fit than a linear function does.‘Vegetation class’ is not an obvious choice as an extra explanatory variable for the regression, as it is only a convenient label for vegetation and should not be used as if it were a real independent environmental variable. In general, indicator values of plant species should be calibrated against environmental variables with great care. This implies that researchers should have knowledge about the ecological demands plants make on their environment, as well as about the spatial and temporal variability of this environment.     

Plant-plant interactions and environmental change

Natural systems are being subjected to unprecedented rates of change and unique pressures from a combination of anthropogenic environmental change drivers. Plant-plant interactions are an important part of the mechanisms governing the response of plant species and communities to these drivers. For example, competition plays a central role in mediating the impacts of atmospheric nitrogen deposition, increased atmospheric carbon dioxide concentrations, climate change and invasive nonnative species. Other plant-plant interaction processes are also being recognized as important factors in determining the impacts of environmental change, including facilitation and evolutionary processes associated with plant-plant interactions. However, plant-plant interactions are not the only factors determining the response of species and communities to environmental change drivers - their activity must be placed within the context of the wide range of factors that regulate species, communities and ecosystems. A major research challenge is to understand when plant-plant interactions play a key role in regulating the impact of environmental change drivers, and the type of role that plant-plant interactions play. Although this is a considerable challenge, some areas of current research may provide the starting point to achieving these goals, and should be pursued through large-scale, integrated, multisite experiments.     

Defining the limits to local density: alternative views of abundance–environment relationships

1. In many ecosystems, the local abundance of organisms is spatially heterogeneous. Ecologists often seek to explain this variation by modelling the central tendency of abundance as a function of a single dominant factor (central response, CR). An alternative approach is to model maximum and minimum abundance in relation to the dominant factor (limiting response, LR), thereby acknowledging that multiple factors may constrain abundance and create scatter in the relationship. In many ecosystems, including streams, abundance–environment relationships are traditionally expected to be CR models with a single dominant factor determining local abundance, but this hypothesis lacks rigorous test. This omission is of concern because CR modelled relationships form the foundations of many ecological tests, predictive and management tools that, consequently, may provide erroneous interpretations. 2. In a survey designed to minimise variance in the data, we related densities of three taxa of stream invertebrates to near‐bed flow. Data were analysed using both ordinary least squares (OLS) regression and quantile regression, which have different characteristic results depending on whether the relationship is better described as a CR or LR model. For all three taxa, invertebrate responses to flow conformed most closely to LR models and were best described by quantile regression, although OLS regression revealed broadly similar general trends in this case. 3. Based on the statistically modelled relationships, we were able to hypothesise which ecological mechanisms limited maximum abundance at this site, including dislodgement with high flow (Heptageniidae, Leuctridae), reduced oxygen uptake at low flow (Baetidae) and reduced provision of interstitial spaces at high flow (Leuctridae). Scatter in the relationships was attributable to multiple factors that may operate at different scales, including inter‐individual variation, alternative environmental gradients and biotic processes at the patch scale, and potential constraints of dispersal and settlement operating outside the patch. 4. We illustrate how the choice of CR or LR model to describe abundance–environment relationships may affect tests of ecological theory and applied ecology. If our results are typical of other streams and other types of systems, greater use of LR models may transform the way many ecologists view ecosystems, thus having positive consequences for survey and experimental designs and highlighting weaknesses in existing management tools that are underpinned by CR models.     

Ecological assessment of vegetation from a nature reserve using regional reference data and indicator scores

A method is presented for ecological assessment of botanical sample data from a nature reserve network. The approach uses regional floristic survey data for a specific biotope as a context for spatial and temporal comparison. Assessments are based upon floristic similarity to reference vegetation types and indicator scores that summarise multivariate plant species data in relation to important environmental gradients. The approach was implemented as a software tool using floristic survey data for soligenous mires in a UK region. Plant community monitoring data were assessed against reference communities from this regional baseline to illustrate the potential advantages of the method. These include; (a) allowing links to be made between multivariate plant species data and measurements of environmental drivers, (b) providing realistic assessments of spatial and temporal differences because comparisons are against typical values of indicator scores for the region, (c) providing the scope for setting realistic criteria for vegetation monitoring.     

苔藓植物对环境变化的响应及适应性研究进展

苔藓植物由于其结构相对简单,对环境变化的反应较为敏感,是一类良好的生物指示植物.本文综述了水分、光照、温度等方面的环境因子变化对苔藓植物的影响以及苔藓植物对环境污染的响应及适应的最近研究进展,以期促进国内深入开展苔藓植物对环境污染和全球变化的响应、适应及其生态指示作用等研究.     

苔藓植物对环境变化的影响及适应性研究进展

苔藓植物由于其结构相对简单,对环境变化的反应较为敏感,是一类良好的生物指示植物,本文综述了水分、光照、温度等方面的环境因子变化对苔藓植物的影响以及苔藓植物对环境污染的响应及适应的最近研究进展,以期促进国内深入开展苔藓植物对环境污染和全球变化的响应、适应及其生态指示作用等研究。     

基于生态需水保障的农业生态补偿标准

面向流域农业需水和生态需水间的矛盾问题和协调发展的要求,提出了基于生态需水保障的农业生态补偿标准计算方法。其中考虑农业用水定额计算基于生态需水保障的农业用水短缺,引入水分生产函数模型建立保障生态需水量产生的农业用水短缺与产量损失间的关系,根据不同季节作物产量响应系数的变化,定量确定具有时间和等级差异性的农业生态补偿标准。以保障黄河口生态需水引起的山东引黄灌区农业损失补偿标准分析为实例,计算了冬小麦和夏玉米种植户不同等级的生态补偿标准。结论认为,农业生态补偿标准需根据不同的来水过程及生态需水等级确定,面积稳定和保障功能显著的粮食作物应作为补偿标准计算的依据。     

Modeling baseline conditions of ecological indicators: Marine renewable energy environmental monitoring

Ecological indicators are often collected to detect and monitor environmental change. Statistical models are used to estimate natural variability, pre-existing trends, and environmental predictors of baseline indicator conditions. Establishing standard models for baseline characterization is critical to the effective design and implementation of environmental monitoring programs. An anthropogenic activity that requires monitoring is the development of Marine Renewable Energy sites. Currently, there are no standards for the analysis of environmental monitoring data for these development sites. Marine Renewable Energy monitoring data are used as a case study to develop and apply a model evaluation to establish best practices for characterizing baseline ecological indicator data. We examined a range of models, including six generalized regression models, four time series models, and three nonparametric models. Because monitoring data are not always normally distributed, we evaluated model ability to characterize normal and non-normal data using hydroacoustic metrics that serve as proxies for ecological indicator data. The nonparametric support vector regression and random forest models, and parametric state-space time series models generally were the most accurate in interpolating the normal metric data. Support vector regression and state-space models best interpolated the non-normally distributed data. If parametric results are preferred, then state-space models are the most robust for baseline characterization. Evaluation of a wide range of models provides a comprehensive characterization of the case study data, and highlights advantages of models rarely used in Marine Renewable Energy environmental monitoring. Our model findings are relevant for any ecological indicator data with similar properties, and the evaluation approach is applicable to any monitoring program.     

Weighted averaging,logistic regression and the Gaussian response model

The indicator value and ecological amplitude of a species with respect to a quantitative environmental variable can be estimated from data on species occurrence and environment. A simple weighted averaging (WA) method for estimating these parameters is compared by simulation with the more elaborate method of Gaussian logistic regression (GLR), a form of the generalized linear model which fits a Gaussian-like species response curve to presence-absence data. The indicator value and the ecological amplitude are expressed by two parameters of this curve, termed the optimum and the tolerance, respectively. When a species is rare and has a narrow ecological amplitude — or when the distribution of quadrats along the environmental variable is reasonably even over the species' range, and the number of quadrats is small — then WA is shown to approach GLR in efficiency. Otherwise WA may give misleading results. GLR is therefore preferred as a practical method for summarizing species' distributions along environmental gradients. Formulas are given to calculate species optima and tolerances (with their standard errors), and a confidence interval for the optimum from the GLR output of standard statistical packages.Nomenclature follows Heukels-van der Meijden (1983).We would like to thank Drs I. C. Prentice, N. J. M. Gremmen and J. A. Hoekstra for comments on the paper. We are grateful to Ir. Th. A. de Boer (CABO, Wageningen) for permission to use the data of the first example.     

Shift in a Large River Fish Assemblage: Body-Size and Trophic Structure Dynamics

As the intensity and speed of environmental change increase at both local and global scales it is imperative that we gain a better understanding of the ecological implications of community shifts. While there has been substantial progress toward understanding the drivers and subsequent responses of community change (e.g. lake trophic state), the ecological impacts of food web changes are far less understood. We analyzed Wabash River fish assemblage data collected from 1974-2008, to evaluate temporal variation in body-size structure and functional group composition. Two parameters derived from annual community size-spectra were our major response variables: (1) the regression slope is an index of ecological efficiency and predator-prey biomass ratios, and (2) spectral elevation (regression midpoint height) is a proxy for food web capacity. We detected a large assemblage shift, over at least a seven year period, defined by dramatic changes in abundance (measured as catch-per-unit-effort) of the dominant functional feeding groups among two time periods; from an assemblage dominated by planktivore-omnivores to benthic invertivores. There was a concurrent increase in ecological efficiency (slopes increased over time) following the shift associated with an increase in large-bodied low trophic level fish. Food web capacity remained relatively stable with no clear temporal trends. Thus, increased ecological efficiency occurred simultaneous to a compensatory response that shifted biomass among functional feeding groups.     

X‐ray computed tomography and its potential in ecological research: A review of studies and optimization of specimen preparation

Imaging techniques are a cornerstone of contemporary biology. Over the last decades, advances in microscale imaging techniques have allowed fascinating new insights into cell and tissue morphology and internal anatomy of organisms across kingdoms. However, most studies so far provided snapshots of given reference taxa, describing organs and tissues under “idealized” conditions. Surprisingly, there is an almost complete lack of studies investigating how an organism′s internal morphology changes in response to environmental drivers. Consequently, ecology as a scientific discipline has so far almost neglected the possibilities arising from modern microscale imaging techniques. Here, we provide an overview of recent developments of X‐ray computed tomography as an affordable, simple method of high spatial resolution, allowing insights into three‐dimensional anatomy both in vivo and ex vivo. We review ecological studies using this technique to investigate the three‐dimensional internal structure of organisms. In addition, we provide practical comparisons between different preparation techniques for maximum contrast and tissue differentiation. In particular, we consider the novel modality of phase contrast by self‐interference of the X‐ray wave behind an object (i.e., phase contrast by free space propagation). Using the cricket Acheta domesticus (L.) as model organism, we found that the combination of FAE fixative and iodine staining provided the best results across different tissues. The drying technique also affected contrast and prevented artifacts in specific cases. Overall, we found that for the interests of ecological studies, X‐ray computed tomography is useful when the tissue or structure of interest has sufficient contrast that allows for an automatic or semiautomatic segmentation. In particular, we show that reconstruction schemes which exploit phase contrast can yield enhanced image quality. Combined with suitable specimen preparation and automated analysis, X‐ray CT can therefore become a promising quantitative 3D imaging technique to study organisms′ responses to environmental drivers, in both ecology and evolution.     

生态阈值确定方法综述

生态系统在环境条件变化时表现出的剧变或阈值现象是当前生态学研究的热点,但是生态阈值定量检测的困难阻碍了这一主题的研究与应用。本文从典型案例入手,通过分析潜在生态阈值的S型曲线式、补给-压力式和跃迁式驱动-响应机理,归纳了局部加权回归散点平滑法、分段回归、高斯模型、拐点分析软件、稳态转换检测软件、指示种阈值分析和系统动力学仿真模型7种生态阈值确定方法,并评述了其优缺点和适用性,以期为生态阈值的定量分析研究提供方法借鉴。