Spatio-temporal analysis of alpine ecotones: A spatial explicit model targeting altitudinal vegetation shifts

There is general agreement in literature that Alpine vegetation belt ecotones have shown a trend of upward migration in the last few decades. Despite the potential of such shifts as indicators of global change effects in mountain ecosystems, there are relatively few works focused on their assessment in a systematic and spatially explicit way. In this work our aim is to quantify the altitudinal shifts and analyse the spatial pattern dynamics of mountain ecotones. We developed a novel procedure to delineate the current and former state of three characteristic mountain ecotones, which we formalised as forest, tree and tundra lines. Our approach is based on the recognition of altitudinal extreme outposts identified with ecotone locations at a slope scale. The integration of multi-temporal datasets allows the identification and quantification of altitudinal advances and retreats in the outpost locations for a given period. We tested the method in a section of the Italian Alps for the period 1957-2003. Results show a general trend of an increase in altitude for the three ecotones, despite the occurrence of occasional decreases. We estimate decadal altitude increments of 25 m for forest line, 13 m for treeline and 11 m for tundra line. We also identified changes in ecotone spatial morphology between the two dates, with significant implications in connectivity and colonisation dynamics.     

Spatial smoothing techniques for the assessment of habitat suitability

Precise knowledge about factors influencing the habitat suitability of a certain species forms the basis for the implementation of effective programs to conserve biological diversity. Such knowledge is frequently gathered from studies relating abundance data to a set of influential variables in a regression setup. In particular, generalised linear models are used to analyse binary presence/absence data or counts of a certain species at locations within an observation area. However, one of the key assumptions of generalised linear models, the independence of observations is often violated in practice since the points at which the observations are collected are spatially aligned. In this paper, we describe a general framework for semiparametric spatial generalised linear models that allows for the routine analysis of non-normal spatially aligned regression data. The approach is utilised for the analysis of a data set of synthetic bird species in beech forests, revealing that ignorance of spatial dependence actually may lead to false conclusions in a number of situations.

Predicting barrier passage and habitat suitability for migratory fish species

Fish migrate to spawn, feed, seek refuge from predators, and escape harmful environmental conditions. The success of upstream migration is limited by the presence of barriers that can impede the passage of fish. We used a spatially explicit modeling strategy to examine the effects of barriers on passage for 21 native and non-native migratory fish species and the amount of suitable habitat blocked for each species. Spatially derived physical parameter estimates and literature based fish capabilities and tolerances were used to predict fish passage success and habitat suitability. Both the fish passage and the habitat suitability models accurately predicted fish presence above barriers for most common, non-stocked species. The fish passage model predicted that barriers greater than or equal to 6 m block all migratory species. Chinook salmon (Oncorhynchus tshawytscha) was expected to be blocked the least. The habitat suitability model predicted that low gradient streams with intact habitat quality were likely to support the highest number of fish species. The fish passage and habitat suitability models were intended to be used by environmental managers as strategy development tools to prioritize candidate dams for field assessment and make decisions regarding the management of migratory fish populations.     

Reproduction of olive tree habitat suitability for global change impact assessment

The olive tree is so typical of the Mediterranean climate that its presence in a territory qualifies the climate of this as Mediterranean. Many clues indicated that in the past olive cultivation limits moved northward or southward in the Northern Hemisphere according to warmer or cooler climate, respectively. This makes the olive tree cultivation area a possible biological indicator of changes in climate and the identification of the climatological parameters that limit its cultivation plays an important role for climate change impact assessment. In this work, three different approaches were compared, with the aim to compare methodologies suited to predict olive tree distribution over the Mediterranean basin: two classifiers (Random Forest, RF and an Artificial Neural Network, ANN) and a spatial model to infer climatic limiters of plant distribution (CLPD). These methodologies were applied within a framework including a geographical information system (GIS), which spatially defined olive tree cultivated area, and climatological informative layers (average temperature and cumulated rainfall, 50 km × 50 km), which were used as predictor variables. The results indicated that RF achieved on the whole, the lowest classification error (113 misclassified cases on 1906 test cases) followed by ANN (128 cases) and CLPD (153 cases). A validation test, performed over areas out of the Mediterranean basin where olive tree is cultivated (i.e. California and Southern Australia), confirmed the goodness of the RF fitted model in predicting olive tree suitable areas. In general, climatic predictor variables of the coldest and warmest periods of the year were the most significant in determining the limits of suitable olive cultivation area for these methodologies. In particular, temperature of January and July and rainfall of October and July were the climatic predictor variables having highest significance for both RF and ANN. Temperature of January >2 °C, of July >20 °C and cumulated annual rainfall >240 mm were the bounds found in the spatial model. The fitted RF model, coupled with the results of both Regional and General Circulation Model, was finally proposed to assess climate change impact on olive tree cultivated area in the Mediterranean basin.     

Self-organized spatial patterns of vegetation in alpine grasslands

There is increased interest in vegetation spatial pattern as an indicator of transition shifts following catastrophes. Much, however, remains unknown about the mechanisms that underlie spatial pattern formations. In this study, we examined how the spatial heterogeneity of species distributions in the grasslands of the Central Pyrenees and Middle Atlas Mountains is associated with plant species diversity and the importance of self-organizing processes in the control of pattern formations. In the grasslands of the Central Pyrenees and Middle Atlas, the spatial heterogeneity of species distributions increased along a habitat degradation gradient defined by an increase in bare soil. In Central Pyrenees grasslands, however, the increase in heterogeneity was associated with self-organizing bare soil formations, rather than the self-organizing distribution of plant species. In Middle Atlas grasslands, the increased heterogeneity of species spatial distributions was a consequence of the self-organizing capacity of the composing species; the increase in bare soil was randomly distributed. In the more heavily grazed grasslands (Middle Atlas), but not in the more lightly grazed and better preserved ecosystem (Central Pyrenees), plant species richness and diversity declined significantly with an increase in grazing pressure because fewer species were able to colonize empty space. On the contrary, the colonization of bare soil by new species increased the diversity and spatial organization of new colonizing species in Central Pyrenees grassland.     

Evaluating the ability of habitat suitability models to predict species presences

Models predicting species spatial distribution are increasingly applied to wildlife management issues, emphasising the need for reliable methods to evaluate the accuracy of their predictions. As many available datasets (e.g. museums, herbariums, atlas) do not provide reliable information about species absences, several presence-only based analyses have been developed. However, methods to evaluate the accuracy of their predictions are few and have never been validated. The aim of this paper is to compare existing and new presence-only evaluators to usual presence/absence measures.     

Using habitat suitability index and particle dispersion models for early detection of marine invaders.

Eradication and control of invasive species are often possible only if populations are detected when they are small and localized. To be efficient, detection surveys should be targeted at locations where there is the greatest risk of incursions. We examine the utility of habitat suitability index (HSI) and particle dispersion models for targeting sampling for marine pests. Habitat suitability index models are a simple way to identify suitable habitat when species distribution data are lacking. We compared the performance of HSI models with statistical models derived from independent data from New Zealand on the distribution of two nonindigenous bivalves: Theora lubrica and Musculista senhousia. Logistic regression models developed using the HSI scores as predictors of the presence/absence of Theora and Musculista explained 26.7% and 6.2% of the deviance in the data, respectively. Odds ratios for the HSI scores were greater than unity, indicating that they were genuine predictors of the presence/ absence of each species. The fit and predictive accuracy of each logistic model were improved when simulated patterns of dispersion from the nearest port were added as a predictor variable. Nevertheless, the combined model explained, at best, 46.5% of the deviance in the distribution of Theora and correctly predicted 56% of true presences and 50% of all cases. Omission errors were between 6% and 16%. Although statistical distribution models built directly from environmental predictors always outperformed the equivalent HSI models, the gain in model fit and accuracy was modest. High residual deviance in both types of model suggests that the distributions realized by Theora and Musculista in the field data were influenced by factors not explicitly modeled as explanatory variables and by error in the environmental data used to project suitable habitat for the species. Our results highlight the difficulty of accurately predicting the distribution of invasive marine species that exhibit low habitat occupancy and patchy distributions in time and space. Although the HSI and statistical models had utility as predictors of the likely distribution of nonindigenous marine species, the level of spatial accuracy achieved with them may be well below expectations for sensitive surveillance programs.     

Effects of experimental protocol on global vegetation model accuracy: A comparison of simulated and observed vegetation patterns for Asia

Prognostic vegetation models have been widely used to study the interactions between environmental change and biological systems. This study examines the sensitivity of vegetation model simulations to: (i) the selection of input climatologies representing different time periods and their associated atmospheric CO₂ concentrations, (ii) the choice of observed vegetation data for evaluating the model results, and (iii) the methods used to compare simulated and observed vegetation. We use vegetation simulated for Asia by the equilibrium vegetation model BIOME4 as a typical example of vegetation model output. BIOME4 was run using 19 different climatologies and their associated atmospheric CO₂ concentrations. The Kappa statistic, Fuzzy Kappa statistic and a newly developed map-comparison method, the Nomad index, were used to quantify the agreement between the biomes simulated under each scenario and the observed vegetation from three different global land- and tree-cover data sets: the global Potential Natural Vegetation data set (PNV), the Global Land Cover Characteristics data set (GLCC), and the Global Land Cover Facility data set (GLCF). The results indicate that the 30-year mean climatology (and its associated atmospheric CO₂ concentration) for the time period immediately preceding the collection date of the observed vegetation data produce the most accurate vegetation simulations when compared with all three observed vegetation data sets. The study also indicates that the BIOME4-simulated vegetation for Asia more closely matches the PNV data than the other two observed vegetation data sets. Given the same observed data, the accuracy assessments of the BIOME4 simulations made using the Kappa, Fuzzy Kappa and Nomad index map-comparison methods agree well when the compared vegetation types consist of a large number of spatially continuous grid cells. The results of this analysis can assist model users in designing experimental protocols for simulating vegetation.     

A model based on molecular structure descriptors for predicting mutagenicity of organic compounds

A model predictive of the potential mutagenicity of organic compounds was devised by relating mutagenicity data obtained in the Ames reversion test to molecular structure parameters describing their hydrophobic, topological, steric and electronic properties. These included second order valence molecular connectivity index, parachor, molar refraction and polarizability of electrons. A classification rule was calculated, by means of discriminant analysis, using a training set of 117 compounds of various chemical classes. There was agreement between experimental data and theoretical expectations for the majority of compounds (70.9%), with homogeneous figures among the different chemical classes under scrutiny. An exception was represented by halogenated aliphatics with up to 3 C atoms, the mutagenicity of which was poorly predicted by the structural analysis model.     

Nitrogen critical loads for alpine vegetation and terrestrial ecosystem response: are we there yet?

Increases in the deposition of anthropogenic nitrogen (N) have been linked to several terrestrial ecological changes, including soil biogeochemistry, plant stress susceptibility, and community diversity. Recognizing the need to identify sensitive indicators of biotic response to N deposition, we empirically estimated the N critical load for changes in alpine plant community composition and compared this with the estimated critical load for soil indicators of ecological change. We also measured the degree to which alpine vegetation may serve as a sink for anthropogenic N and how much plant sequestration is related to changes in species composition. We addressed these research goals by adding 20, 40, or 60 kg N x ha(-1) x yr(-1), along with an ambient control (6 kg N x ha(-1) x yr(-1) total deposition), to a species-rich alpine dry meadow for an eight-year period. Change in plant species composition associated with the treatments occurred within three years of the initiation of the experiment and were significant at all levels of N addition. Using individual species abundance changes and ordination scores, we estimated the N critical loads (total deposition) for (1) change in individual species to be 4 kg N x ha(-1) yr(-1) and (2) for overall community change to be 10 kg N x ha(-1) x yr(-1). In contrast, increases in NO3- leaching, soil solution inorganic NO3-, and net N nitrification occurred at levels above 20 kg N x ha(-1) x yr(-1). Increases in total aboveground biomass were modest and transient, occurring in only one of the three years measured. Vegetative uptake of N increased significantly, primarily as a result of increasing tissue N concentrations and biomass increases in subdominant species. Aboveground vegetative uptake of N accounted for <40% of the N added. The results of this experiment indicate that changes in vegetation composition will precede detectable changes in more traditionally used soil indicators of ecosystem responses to N deposition and that changes in species composition are probably ongoing in alpine dry meadows of the Front Range of the Colorado Rocky Mountains. Feedbacks to soil N cycling associated with changes in litter quality and species composition may result in only short-term increases in vegetation N pools.     

Coastal sand dune vegetation: An extreme case of species invasion

The coastal sand-dune flora of the Gulf and Caribbean region of Mexico was analyzed to understand differences in floristic composition and richness found along the coast. Each of the 655 species reported was classified according to its ecology and distribution range by checking herbaria specimens, literature and specialists. Three groups were formed: (a) species with predominantly coastal distribution; (b) ruderal or secondary species frequently found inland, common of disturbed areas such as roadsides, abandoned fields or forming part of secondary growths; (c) inland species frequently found in other vegetation types such as tropical dry or seasonal forest and grassland. A total of 71 coastal species, 237 ruderal/secondary and 336 species from other community types were found. The distribution of these groups was analyzed along 44 sites of the Gulf and Caribbean, in the different dune habitats and for the dominant growth forms. Coastal species are more widely distributed; they predominate in habitats with sand movement and the herbaceous component prevails. Ruderal/secondary species and especially those belonging to other vegetation types frequently appear in only one or two sites occupying more protected or stabilized habitats. The two latter groups considerably increase species richness of sand dune flora, but also pose interesting problems for dune conservation.     

Refining logistic regression models for wildlife habitat suitability modeling—A case study with muntjak and goral in the Central Himalayas, India

High quality habitat suitability maps are indispensable for the management and planning of wildlife reserves. This is particularly important for megadiverse developing countries where shortages in skilled manpower and funding may preclude the use of mathematically complex modeling techniques and resource-intensive field surveys. In this study, we propose a simulation based k-fold partitioning and re-substitution approach to refine and update logistic regression models that are widely used for habitat suitability assessment and modeling. We test the modeling strategy using data from a rapid field survey conducted for habitat suitability assessment for muntjak (Muntiacus muntjak) and goral (Naemorrhaedus goral) in the central Himalayas, India. Results obtained from simulations match expectations in terms of model behavior and in terms of published habitat associations of the investigated species. Qualitative comparisons with predictions from the GARP, MaxEnt and Bioclimatic Envelopes modeling systems also show broad agreement with predictions obtained from the proposed technique. The proposed technique is suggested as a rapid-assessment precursor to detailed habitat studies such as patch occupancy modeling in situations where funds or trained manpower are not available.     

Separating habitat invasibility by alien plants from the actual level of invasion

Habitats vary considerably in the level of invasion (number or proportion of alien plant species they contain), which depends on local habitat properties, propagule pressure, and climate. To determine the invasibility (susceptibility to invasions) of different habitats, it is necessary to factor out the effects of any confounding variables such as propagule pressure and climate on the level of invasion. We used 20 468 vegetation plots from 32 habitats in the Czech Republic to compare the invasibility of different habitats. Using regression trees, the proportion of alien plants, including archaeophytes (prehistoric to medieval invaders) and neophytes (recent invaders), was related to variables representing habitat properties, propagule pressure, and climate. The propagule pressure was expressed as the proportion of surrounding urban and industrial or agricultural land, human population density, distance from a river, and history of human colonization in the region. Urban and industrial land use had a positive effect on the proportion of both archaeophytes and neophytes. Agricultural land use, higher population density, and longer history of human impact positively affected the proportion of archaeophytes. Disturbed human-made habitats with herbaceous vegetation were most invaded by both groups of aliens. Neophytes were also relatively common in disturbed woody vegetation, such as broad-leaved plantations, forest clearings, and riverine scrub. These habitats also had the highest proportion of aliens after removing the effect of propagule pressure and climate, indicating that they are not only the most invaded, but also most invasible. These habitats experience recurrent disturbances and are rich, at least temporarily, in available nutrients, which supports the hypothesis that fluctuating resources are the major cause of habitat invasibility. The least invaded habitats were mires and alpine-subalpine grasslands and scrub. After removing the effect of propagule pressure and climate, some habitats actually invaded at an intermediate level had very low proportions of aliens. This indicates that these habitats (e.g., dry, wet, and saline grasslands, base-rich fens, and broad-leaved deciduous woodlands) are resistant to invasion.     

放牧管理对藏北高寒沼泽草甸群落结构的影响

采用样带法研究了冬季放牧、冬春季放牧和全年放牧3种放牧管理模式对西藏高寒沼泽草甸高度、盖度、地上生物量及群落结构的影响.研究表明,3种放牧管理模式下的高寒沼泽草甸植被群落物种数差异不显著（p〉0.05）,高度、盖度和地上生物量差异显著（p〈0.05）。冬季放牧、冬春季放牧和全年放牧沼泽草甸地上生物量依次降低。放牧管理对沼泽草甸群落多样性影响较大,不同放牧管理模式下的沼泽草甸Shannon-Wiener指数差异显著（p〈0.05）,冬春放牧沼泽草甸Shannon-Wiener指数较冬季放牧和全年放牧沼泽草甸低。冬季放牧和全年放牧沼泽草甸Margalef指数和Pielou指数差异不显著,显著（p〈0.05）高于冬春季放牧沼泽草甸。     

基于ETM+图像的植被覆盖度遥感估算模型

植被覆盖度(VFC)的定量遥感是多种地表过程研究的迫切需要.文章选用南京市一幅Landsat 7 ETM 图像,经大气校正后提取了归一化植被指数(NDVI),与地面实测的植被覆盖度进行回归分析,建立了1～4次多项式关系模型.结果表明,NDVI与VFC呈极显著的正相关关系(r = 0.874, P < 0.001).在NDVI-VFC的1～4次多项式关系模型中,模型幂次越高,拟合程度越好.综合考虑模型的精度和稳定性,3次多项式模型作为最优模型推荐使用:VFC = -1.3438 NDVI 3 0.9774 NDVI 2 0.9988 NDVI 0.1507 (R2 = 0.7961, RMSE = 0.1094),该模型精度在植被中等密集区域(VFC＝0.4～0.8)最高,植被稀疏区域(VFC < 0.4)最低,植被密集区域(VFC > 0.8)居中.模型可直接用于全图像的VFC计算,并可通过植被指数的校准,进行推广使用.     

A hierarchical,multi-scale,management-responsive model of Mediterranean vegetation dynamics

Ecosystems of the Mediterranean basin are characterized by a heterogeneous and dynamic landscape mosaic of vegetation formations. This landscape has been shaped over millenia by disturbances associated with agropastoral land use: clearing, grazing and burning, and by regeneration processes of the natural woody vegetation. The ability to predict the effects of management decisions on the structure and composition of the vegetation is essential for present and future land management. To improve this ability we developed a hierarchical multi-scale, management-responsive model of vegetation dynamics.     

高寒草甸不同植被类型土壤全氮含量变化动态分析

采用凯氏定氮法对高寒草甸不同植被类型土壤全氮进行季节动态测定分析,结果表明:在整个生长季中0～20 cm层土壤全氮质量分数的顺序为:藏嵩草沼泽化草甸(Kobresia-swamp meadow)>露梅灌丛草甸(Dasiphoru fruticosa shrubs)>人工燕麦草地(Avena sativa artficial grassland)>矮嵩草草甸(Kobresia humilis meadow)>矮嵩草退化草地(Kobresia humilis-degraded grassland).原生植被草甸类型下单位面积土壤全氮含量远高于退化草地.藏嵩草沼泽化草甸土壤每平方米的全氮含量最高,达到0.712 kg,金露梅草甸次之,两者之间差异性不显著(p>0.05);其他三种草地类型单位面积土壤全氮含量差异性显著(p<0.05);原生草甸矮嵩草草甸每平方米全氮平均含量为0.406 kg,而退化的矮嵩草草地每平方米全氮平均含量为0.301 kg,可以推算,土地退化导致土壤全氮流失的量为0.105kg,即高寒草地退化导致25.86%氮流失.随着季节的变化,土壤全氮质量分数随生长季均有所增加,最高值都出现在8月份,但各月份之间土壤全氮质量分数变化差异性不显著(p>0.05).原生植被0～10 cm层土壤全氮含量高于10～2O cm层,人工草地与退化草地差异性不显著.     

A method for assigning species into groups based on generalized Mahalanobis distance between habitat model coefficients

Habitat association models are commonly developed for individual animal species using generalized linear modeling methods such as logistic regression. We considered the issue of grouping species based on their habitat use so that management decisions can be based on sets of species rather than individual species. This research was motivated by a study of western landbirds in northern Idaho forests. The method we examined was to separately fit models to each species and to use a generalized Mahalanobis distance between coefficient vectors to create a distance matrix among species. Clustering methods were used to group species from the distance matrix, and multidimensional scaling methods were used to visualize the relations among species groups. Methods were also discussed for evaluating the sensitivity of the conclusions because of outliers or influential data points. We illustrate these methods with data from the landbird study conducted in northern Idaho. Simulation results are presented to compare the success of this method to alternative methods using Euclidean distance between coefficient vectors and to methods that do not use habitat association models. These simulations demonstrate that our Mahalanobis-distance-based method was nearly always better than Euclidean-distance-based methods or methods not based on habitat association models. The methods used to develop candidate species groups are easily explained to other scientists and resource managers since they mainly rely on classical multivariate statistical methods.