南昌市植被覆盖度时空演变及其对非气候因素的响应

植被是陆地生态系统的重要组成部分,植被覆盖在空间上的差异是气候和人类活动交互作用的结果。随着城市扩张,人类活动的加剧及不合理的土地利用方式导致了很多生态问题,对植被覆盖有重大影响。基于地形调节植被指数的像元二分模型,利用3期landsat-5 TM影像图分析南昌市植被覆盖度时空演变特征,并结合DEM数据分析植被覆盖度及变化的地形梯度分异规律,利用3期土地利用图量化植被覆盖度变化对土地利用方式转变的响应。结果显示:1)研究区2001—2010年植被覆盖度从0.54下降为0.42,总体上呈退化趋势,2005年之后植被退化有所减缓;2)植被覆盖度的地形梯度变化显著。植被覆盖度与高程呈高度的正相关性,在坡度0—22°梯度带呈现较高的正相关,在坡度22—40°梯度带呈现较高的负相关。80%以上植被覆盖变化集中在海拔30 m以下、坡度4°以下的区域;3)植被覆盖度变化是地形与土地利用综合作用的结果。在平原低丘区,土地利用行为是植被覆盖变化的主导因素。城市的建设和扩张导致占用耕地、林地和草地,以及大面积的撂荒、伐林等土地活动对植被覆盖退化的贡献率为50%以上,是植被覆盖退化的主要原因,而退耕还林还草、废弃地复垦、后备资源开发为植被覆盖增加的主要原因。可为平原低丘区生态环境监测和构建环境友好型土地利用模式提供科学依据。     

Empirical realised niche models for British higher and lower plants – development and preliminary testing

Question: Can useful realised niche models be constructed for British plant species using climate, canopy height and mean Ellenberg indices as explanatory variables? Location: Great Britain. Methods: Generalised linear models were constructed using occurrence data covering all major natural and semi‐natural vegetation types (n=40 683 quadrat samples). Paired species and soil records were only available for 4% of the training data (n=1033) so modelling was carried out in two stages. First, multiple regression was used to express mean Ellenberg values for moisture, pH and fertility, in terms of direct soil measurements. Next, species presence/absence was modelled using mean indicator scores, cover‐weighted canopy height, three climate variables and interactions between these factors, but correcting for the presence of each target species in training plots to avoid circularity. Results: Eight hundred and three higher plants and 327 bryophytes were modelled. Thirteen per cent of the niche models for higher plants were tested against an independent survey dataset not used to build the models. Models performed better when predictions were based only on indices derived from the species composition of each plot rather than measured soil variables. This reflects the high variation in vegetation indices that was not explained by the measured soil variables. Conclusions: The models should be used to estimate expected habitat suitability rather than to predict species presence. Least uncertainty also attaches to their use as risk assessment and monitoring tools on nature reserves because they can be solved using mean environmental indicators calculated from the existing species composition, with or without climate data.     

A New Approach to Diversity Indices – Modeling and Mapping Plant Biodiversity of Nallihan (A3-Ankara/Turkey) Forest Ecosystem in Frame of Geographic Information Systems

Modeling and mapping possibilities of Shannon–Wiener, Simpson, and number of species (NS) indices were researched using geographic information systems (GIS) and remote sensing (RS) tools in Nallihan forest ecosystem of Turkey. The relationships between the indices and a number of independent variables such as topography, geology, soil, climate, normalized difference vegetation index (NDVI), and land cover were investigated to understand relationships between plant diversity and ecosystem. Georeferenced field data from the established 56 quadrats (50 × 20 m) were used to calculate the indices. Principle component analysis (PCA) and multiple regression were employed for data reduction and model development, respectively. Three diversity maps were produced using the developed models. Residual maps and logical interpretations in ecological point of view were used to test the validity of the models. Elevation and climatic factors formed the most important components that are effective determinants of plant species diversity, but geological formations, soil, land cover and land-use characteristics also influenced plant diversity. Considering the different responses of the models, Shannon–Wiener (SWI) and NS models were found suitable for rare cover types, while Simpson (SIMP) model might be appropriate for single dominant land covers in the study area.     

Vegetation cover—another dominant factor in determining global water resources in forested regions

Forested catchments provide critically important water resources. Due to dramatic global forest change over the past decades, the importance of including forest or vegetation change in the assessment of water resources under climate change has been highly recognized by Intergovernmental Panel on Climate Change (IPCC); however, this importance has not yet been examined quantitatively across the globe. Here, we used four remote sensing‐based indices to represent changes in vegetation cover in forest‐dominated regions, and then applied them to widely used models: the Fuh model and the Choudhury‐Yang model to assess relative contributions of vegetation and climate change to annual runoff variations from 2000 to 2011 in forested landscape (forest coverage >30%) across the globe. Our simulations show that the global average variation in annual runoff due to change in vegetation cover is 30.7% ± 22.5% with the rest attributed to climate change. Large annual runoff variation in response to vegetation change is found in tropical and boreal forests due to greater forest losses. Our simulations also demonstrate both offsetting and additive effects of vegetation cover and climate in determining water resource change. We conclude that vegetation cover change must be included in any global models for assessing global water resource change under climate change in forest‐dominant areas.     

中国遥感干旱指数时空特征及其对气候和地表覆盖变化的响应

干旱严重影响植被生长,威胁粮食安全,基于遥感计算的植被状态指数（Vegetation Condition Index,VCI）、温度状态指数（Temperature Condition Index,TCI）和植被健康指数（Vegetation Health Index,VHI）是常用的干旱指数,被广泛应用于干旱监测。为了探究近年来我国干旱特征及其对气候和地表覆盖变化的响应,分析了2003-2016年期间VCI、TCI和VHI的时空变化特征;采用最小二乘（OLS）和偏相关分析方法分析了这些指数对气候和地表覆盖变化的响应。基于上述干旱指数计算的干旱频率表明,中温带中部和南温带等地区干旱发生频率高,干旱指数变化趋势表明在2003-2016年期间中国大部分地区干旱缓解,但在中温带、南温带和高原气候区等局部地区干旱加剧;总体而言,干旱指数随着年平均温度的上升和年降水量的降低而减小,VHI与温度和降水量的相关性在不同气候区的一致性优于VCI和TCI;裸土的减少和植被的增加导致干旱指数增大,树木转变为低矮植被干旱指数降低。     

基于土地覆盖和NDVI变化的拉萨河流域生境质量评估

气候变化和人类活动导致的土地覆盖和植被变化都会对生境质量产生影响。青藏高原是众多珍稀高原动植物的栖息地,具有重要的生物多样性维持价值。拉萨河流域是青藏高原经济最发达、人口最密集的核心地区,人类活动对生境质量带来的胁迫和压力持续增加。为揭示近些年来土地覆盖和植被变化对拉萨河流域生境质量的影响,选择生长季NDVI作为植被变化的指示因子,通过对不同植被类型各年份的生境适宜度进行修正,利用In VEST模型评估了拉萨河流域1990—2015年的生境质量时空变化。研究结果表明,1990—2015年拉萨河流域土地覆盖变化整体相对较小,其中人工表面和湿地面积增幅相对较大,分别为82.65%和32.40%;土地覆盖变化的转移方向主要为稀疏草地转化为草原和草甸、耕地转化为人工表面以及冰川/积雪转化为荒地。植被变化方面,1990—2000年,除流域中上游的裸岩、裸土地区和念青唐古拉山地区外,流域NDVI整体有较显著上升;而2000年以后略有下降。从生境质量的空间分布来看,高质量生境主要分布在流域下游、念青唐古拉山南侧河谷地区以及拉萨河源头等地区,低质量生境主要分布在拉萨市市辖区及周边、林周县县城及周边,以及流域中上游的荒地等地区。从时间变化上来看,1990—2000年,拉萨河流域整体生境质量指数从0.51上升到0.57; 2010年和2015年整体生境质量指数分别为0.56和0.55,较2000年略有下降。相比于土地覆盖变化,NDVI对生境质量变化的影响更为显著。     

Fine‐scale determinants of butterfly species richness and composition in a mountain region

Aim Global patterns of species richness are often considered to depend primarily on climate. We aimed to determine how topography and land cover affect species richness and composition at finer scales. Location Sierra de Guadarrama (central Iberian Peninsula). Methods We sampled the butterfly fauna of 180 locations (89 in 2004, 91 in 2005) at 600–2300 m elevation in a region of 10800 km². We recorded environmental variables at 100‐m resolution using GIS, and derived generalized linear models for species density (number of species per unit area) and expected richness (number of species standardized to number of individuals) based on variables of topoclimate (elevation and insolation) or land cover (vegetation type, geology and hydrology), or both (combined). We evaluated the models against independent data from the alternative study year. We also tested for differences in species composition among sites and years using constrained ordination (canonical correspondence analysis), and used variation partitioning analyses to quantify the independent and combined roles of topoclimate and land cover. Results Topoclimatic, land cover and combined models were significantly related to observed species density and expected richness. Topoclimatic and combined models outperformed models based on land cover variables, showing a humped elevational diversity gradient. Both topoclimate and land cover made significant contributions to models of species composition. Main conclusions Topoclimatic factors may dominate species richness patterns in regions with pronounced elevational gradients, as long as large areas of natural habitat remain. In contrast, both topoclimate and land cover may have important effects on species composition. Biodiversity conservation in mountainous regions therefore requires protection and management of natural habitats over a wide range of topoclimatic conditions, which may assist in facilitating range shifts and alleviating declines in species richness related to climate change.     

藏北高原地表覆盖时空动态及其对气候变化的响应

利用2001—2008年逐年的MODIS地表覆盖类型产品,根据藏北高原地表覆被特征对原始数据进行合并处理,得到每年藏北高原地表覆盖类型图;运用分类统计、动态转移矩阵、景观格局指数方法分析藏北高原地表覆盖类型的变化,并结合研究区内气象台站观测数据分析地表覆盖类型转化对气候变化的响应特征.结果表明:研究期间,由于气候变暖速率的加快,研究区冰川雪被消融加速,冰川面积迅速萎缩,融化的雪水汇集到高原湖盆,使湖面水位上升,湖泊面积增加,部分被淹没的草地形成湿地;植被覆盖状况没有表现出明显的变好或退化趋势,2001—2004年为气候暖湿化阶段,荒漠裸地减少、稀疏草地和草地覆盖面积增加,2006—2007年为气候暖干化阶段,荒漠面积增加、稀疏草地面积减小;2001—2008年,藏北高原景观破碎度减小,地表覆盖异质性降低,且各类型所占比例的差异有所加大.     

A Multilevel Analysis of the Impact of Land Use on Interannual Land-Cover Change in East Africa

Abstract The aim of this study was to characterize the short-term land-cover change processes that were detected in Eastern Africa, based on a set of change metrics that allow for the quantification of interannual changes in vegetation productivity, changes in vegetation phenology and a combination of both. We tested to what extent land use, fire activity and livestock grazing modified the vegetation response to short-term rainfall variability in East Africa and how this is reflected in change metrics derived from MODerate Imaging Spectrometer (MODIS) time series of remote sensing data. We used a hierarchical approach to disentangle the contribution of human activities and climate variability to the patterns of short-term vegetation change in East Africa at different levels of organization. Our results clearly show that land use significantly influences the vegetation response to rainfall variability as measured by time series of MODIS data. Areas with different types of land use react in a different way to interannual climate variability, leading to different values of the change indices depending on the land use type. The impact of land use is more reflected in interannual variability of vegetation productivity and overall change in the vegetation, whereas changes in phenology are mainly driven by climate variability and affect most vegetation types in similar ways. Our multilevel approach led to improved models and clearly demonstrated that climate influence plays at a different scale than land use, fire and herbivore grazing. It helped us to understand dynamics within and between biomes in the study area and investigate the relative importance of different factors influencing short-term variability in change indices at different scales.     

Mountain vegetation change quantification using surface landscape metrics in Lancang watershed,China

Land cover and vegetation change are among the most important aspects of environmental change. Vegetation change can be quantified by landscape pattern indices (LPI). Landscape indices are routinely calculated using planar land use/land cover (LU/LC) maps, obtained by the projection of a non-flat landscape surface into a two-dimensional Cartesian space. Especially in mountainous areas, quantification on planar maps can lead to underestimation of vegetation and land cover changes. Hoechstetter et al. (2008) developed a method to compute LPIs in a surface structure by calculating landscape patch surface area and surface perimeter from digital elevation models (DEM). As yet there have been no applications of these surface landscape indices on land use/land cover and vegetation change quantification. The objectives of this study are to (1) choose a LPI method (surface metrics pattern analysis or common planimetric metrics pattern analysis) for vegetation change quantification; and (2) employ the selected surface LPI method to assess vegetation pattern change in two mountainous areas of the Lancang watershed, Yunnan Province, China. The results show that the surface approach to estimate changes of class area (CA), mean patch area (MPA), and mean Euclidean Near-Neighbor distance (MENN) may obtain more accurate results for quantifying vegetation change in steep mountain areas. Forest fragmentation increased significantly over time in the two different mountainous study areas. The patches of two land cover classes, (i) agricultural land and (ii) low density forest and tall shrubs, became more aggregated in the northern (temperate) study area. In the southern (tropical) study area, rubber plantations increased considerably in size and became more aggregated.     

Native vegetation structure,landscape features and climate shape non-native plant richness and cover in New Zealand native shrublands

Aim

Studies investigating the determinants of plant invasions rarely examine multiple factors and often only focus on the role played by native plant species richness. By contrast, we explored how vegetation structure, landscape features and climate shape non-native plant invasions across New Zealand in mānuka and kānuka shrublands.

Location

New Zealand.

Method

We based our analysis on 247 permanent 20 × 20-m plots distributed across New Zealand surveyed between 2009 and 2014. We calculated native plant species richness and cumulative cover at ground, understorey and canopy tiers. We examined non-native species richness and mean species ground cover in relation to vegetation structure (native richness and cumulative cover), landscape features (proportion of adjacent anthropogenic land cover, distance to nearest road or river) and climate. We used generalized additive models (GAM) to assess which variables had greatest importance in determining non-native richness and mean ground cover and whether these variables had a similar effect on native species in the ground tier.

Results

A positive relationship between native and non-native plant species richness was not due to their similar responses to the variables examined in this study. Higher native canopy richness resulted in lower non-native richness and mean ground cover, whereas higher native ground richness was associated with higher native canopy richness. Non-native richness and mean ground cover increased with the proportion of adjacent anthropogenic land cover, whereas for native richness and mean ground cover, this relationship was negative. Non-native richness increased in drier areas, while native richness was more influenced by temperature.

Main Conclusions

Adjacent anthropogenic land cover seems to not only facilitate non-native species arrival by being a source of propagules but also aids their establishment as a result of fragmentation. Our results highlight the importance of examining both cover and richness in different vegetation tiers to better understand non-native plant invasions.     

我国三北地区植被变化的动因分析

地表植被变化是气候变化、人类活动等多种因素共同作的结果。然而,以往的研究要么集中在与气候变化有关的气象因素,要么集中在与人类活动有关的人为因素,鲜有基于长期数据监测下对自然与社会因素之间相互作用的定量评估。因此,气候变化和人为因素对地表植被变化的相互作用并不明确,各个因素对植被变化影响的量化贡献仍然不确定。为了评价生态修复项目对荒漠化防治的效果、以及在土地荒漠化防治中自然与社会因素对我国植被变化的影响、及其复杂的相互作用机理,该研究应用卫星遥感影像资料,通过面板数据混合回归模型大数据分析方法,计算了1983年至2012年气候变化和人类活动对我国北方地区植被变化的贡献率。结果表明,气候变化和人类活动对NDVI变化均有重要作用,其中人类活动对植被覆盖度变化的影响占58.2%—90.4%、气候变化占9.6%—41.8%;不同地区表现出不同的地理分异特征,并存在时滞效应。由此可见,荒漠化防治必须充分考虑不同因素的综合作用和地域特征,才能取得事半功倍的效果。研究结果较好地体现了卫星遥感影像资料在大尺度(省域尺度)下与社会经济统计指标的融合,为进一步中尺度(县域尺度)研究提供了方法借鉴。     

Spatial heterogeneity of tundra vegetation response to recent temperature changes

The spatial heterogeneity of recent decadal dynamics in vegetation greenness and biomass in response to changes in summer warmth index (SWI) was investigated along spatial gradients on the Arctic Slope of Alaska. Image spatial analysis was used to examine the spatial pattern of greenness dynamics from 1991 to 2000 as indicated by variations of the maximum normalized difference vegetation index (Peak NDVI) and time‐integrated NDVI (TI‐NDVI) along latitudinal gradients. Spatial gradients for both the means and temporal variances of the NDVI indices for 0.1° latitude intervals crossing three bioclimate subzones were analyzed along two north–south Arctic transects. NDVI indices were generally highly variable over the decade, with great heterogeneity across the transects. The greatest variance in TI‐NDVI was found in low shrub vegetation to the south (68.7–68.8°N) and corresponded to high fractional cover of shrub tundra and moist acidic tundra (MAT), while the greatest variance in Peak‐NDVI predominately occurred in areas dominated by wet tundra (WT) and moist nonacidic tundra (MNT). Relatively high NDVI temporal variances were also related to specific transitional areas between dominant vegetation types. The regional temporal variances of NDVI from 1991 to 2000 were largely driven by meso‐scale climate dynamics. The spatial heterogeneity of the NDVI variance was mostly explained by the fractional land cover composition, different responses of each vegetation type to climate change, and patterned ground features. Aboveground plant biomass exhibited similar spatial heterogeneity as TI‐NDVI; however, spatial patterns are slightly different from NDVI because of their nonlinear relationship.     

Spatial relationship between climatologies and changes in global vegetation activity

Vegetation forms a main component of the terrestrial biosphere and plays a crucial role in land‐cover and climate‐related studies. Activity of vegetation systems is commonly quantified using remotely sensed vegetation indices (VI). Extensive reports on temporal trends over the past decades in time series of such indices can be found in literature. However, little remains known about the processes underlying these changes at large spatial scales. In this study, we aimed at quantifying the spatial relationship between changes in potential climatic growth constraints (i.e. temperature, precipitation and incident solar radiation) and changes in vegetation activity (1982–2008). We demonstrate an additive spatial model with 0.5° resolution, consisting of a regression component representing climate‐associated effects and a spatially correlated field representing the combined influence of other factors, including land‐use change. Little over 50% of the spatial variance could be attributed to changes in climatologies; conspicuously, many greening trends and browning hotspots in Argentina and Australia. The nonassociated model component may contain large‐scale human interventions, feedback mechanisms or natural effects, which were not captured by the climatologies. Browning hotspots in this component were especially found in subequatorial Africa. On the scale of land‐cover types, strongest relationships between climatologies and vegetation activity were found in forests, including indications for browning under warming conditions (analogous to the divergence issue discussed in dendroclimatology).     

Inclusion of soil data improves the performance of bioclimatic envelope models for insect species distributions in temperate Europe

Aim To analyse the effect of the inclusion of soil and land‐cover data on the performance of bioclimatic envelope models for the regional‐scale prediction of butterfly (Rhopalocera) and grasshopper (Orthoptera) distributions. Location Temperate Europe (Belgium). Methods Distributional data were extracted from butterfly and grasshopper atlases at a resolution of 5 km for the period 1991–2006 in Belgium. For each group separately, the well‐surveyed squares (n = 366 for butterflies and n = 322 for grasshoppers) were identified using an environmental stratification design and were randomly divided into calibration (70%) and evaluation (30%) datasets. Generalized additive models were applied to the calibration dataset to estimate occurrence probabilities for 63 butterfly and 33 grasshopper species, as a function of: (1) climate, (2) climate and land‐cover, (3) climate and soil, and (4) climate, land‐cover and soil variables. Models were evaluated as: (1) the amount of explained deviance in the calibration dataset, (2) Akaike’s information criterion, and (3) the number of omission and commission errors in the evaluation dataset. Results Information on broad land‐cover classes or predominant soil types led to similar improvements in the performance relative to the climate‐only models for both taxonomic groups. In addition, the joint inclusion of land‐cover and soil variables in the models provided predictions that fitted more closely to the species distributions than the predictions obtained from bioclimatic models incorporating only land‐cover or only soil variables. The combined models exhibited higher discrimination ability between the presence and absence of species in the evaluation dataset. Main conclusions These results draw attention to the importance of soil data for species distribution models at regional scales of analysis. The combined inclusion of land‐cover and soil data in the models makes it possible to identify areas with suitable climatic conditions but unsuitable combinations of vegetation and soil types. While contingent on the species, the results indicate the need to consider soil information in regional‐scale species–climate impact models, particularly when predicting future range shifts of species under climate change.     

Satellite surface reflectance improves habitat distribution mapping: a case study on heath and shrub formations in the Cantabrian Mountains (NW Spain)

Aim We aim to map the distribution of four heath and shrub formations constituting habitats of high conservation priority in Europe, whose occurrence is strongly dependent on human activities. Specifically, we assess whether the use of LANDSAT data in habitat distribution modelling may account for land use management, allowing accurate mapping of real distribution patterns. In particular, we explore whether reflectance values may be a better alternative to other remote sensing data traditionally used in modelling approaches (i.e. spectral vegetation indices and classified land cover maps). Finally, we test whether modelling performance is affected by the ecological traits of the dominant species of the target formations. Location Cantabrian Mountains (NW Spain). Methods We generated maps for the four formations (two specialists vs. two generalists) using MaxEnt. First, we ran the models with environmental predictors only (topography, climate, lithology and human disturbances). Then, we compared the advantages of including, in turn, different data derived from LANDSAT imagery: reflectance values (corresponding to different wavelength channels of the multispectral image), a spectral index and a land cover map. We assessed changes in explanatory power and also in the formation’s predicted distribution patterns. Results Formations dominated by specialist species were accurately mapped on a base of environmental variables only, whereas those dominated by generalists were overpredicted. Average mean temperature, southness and distance to urban areas were the variables contributing most in predictions of environmental models. LANDSAT channels increased the accuracy of all models, but mainly those for formations dominated by generalist species. They showed advantages against other remote sensing data traditionally used in modelling approaches. Main conclusions Habitat distribution models allowed accurate mapping of heath and shrub formations. The use of reflectance values as predictors improved the accuracy of the models, particularly for formations dominated by generalist species, supplying environmental information that was otherwise unavailable.     

Factors controlling the spatial species richness pattern of four groups of terrestrial vertebrates in an area between two different biogeographic regions in northern Spain

Aim To examine the influence of environmental variables on species richness patterns of amphibians, reptiles, mammals and birds and to assess the general usefulness of regional atlases of fauna. Location Navarra (10,421 km²) is located in the north of the Iberian Peninsula, in a territory shared by Mediterranean and Eurosiberian biogeographic regions. Important ecological patterns, climate, topography and land‐cover vary significantly from north to south. Methods Maps of vertebrate distribution and climatological and environmental data bases were used in a geographic information systems framework. Generalized additive models and partial regression analysis were used as statistical tools to differentiate (A) the purely spatial fraction, (B) the spatially structured environmental fraction and (C) the purely environmental fraction. In this way, we can evaluate the explanatory capacity of each variable, avoiding false correlations and assessing true causality. Final models were obtained through a stepwise procedure. Results Energy‐related features of climate, aridity and land‐cover variables show significant correlation with the species richness of reptiles, mammals and birds. Mammals and birds exhibit a spatial pattern correlated with variables such as aridity index and vegetation land‐cover. However, the high values of the spatially structured environmental fraction B and the low values of the purely environmental fraction A suggest that these predictor variables have a limited causal relationship with species richness for these vertebrate groups. An increment in land‐cover diversity is correlated with an increment of specific richness in reptiles, mammals and birds. No variables were found to be statistically correlated with amphibian species richness. Main conclusions Although aridity and land‐cover are the best predictor variables, their causal relationship with species richness must be considered with caution. Historical factors exhibiting a similar spatial pattern may be considered equally important in explaining the patterns of species richness. Also, land‐cover diversity appears as an important factor for maintaining biological diversity. Partial regression analysis has proved a useful technique in dealing with spatial autocorrelation. These results highlight the usefulness of coarsely sampled data and cartography at regional scales to predict and explain species richness patterns for mammals and birds. The accuracy of models appears to be related to the range perception of each group and the scale of the information.     

Predicting Plant Diversity Patterns in Madagascar: Understanding the Effects of Climate and Land Cover Change in a Biodiversity Hotspot

Climate and land cover change are driving a major reorganization of terrestrial biotic communities in tropical ecosystems. In an effort to understand how biodiversity patterns in the tropics will respond to individual and combined effects of these two drivers of environmental change, we use species distribution models (SDMs) calibrated for recent climate and land cover variables and projected to future scenarios to predict changes in diversity patterns in Madagascar. We collected occurrence records for 828 plant genera and 2186 plant species. We developed three scenarios, (i.e., climate only, land cover only and combined climate-land cover) based on recent and future climate and land cover variables. We used this modelling framework to investigate how the impacts of changes to climate and land cover influenced biodiversity across ecoregions and elevation bands. There were large-scale climate- and land cover-driven changes in plant biodiversity across Madagascar, including both losses and gains in diversity. The sharpest declines in biodiversity were projected for the eastern escarpment and high elevation ecosystems. Sharp declines in diversity were driven by the combined climate-land cover scenarios; however, there were subtle, region-specific differences in model outputs for each scenario, where certain regions experienced relatively higher species loss under climate or land cover only models. We strongly caution that predicted future gains in plant diversity will depend on the development and maintenance of dispersal pathways that connect current and future suitable habitats. The forecast for Madagascar’s plant diversity in the face of future environmental change is worrying: regional diversity will continue to decrease in response to the combined effects of climate and land cover change, with habitats such as ericoid thickets and eastern lowland and sub-humid forests particularly vulnerable into the future.     

Performance evaluation and cost assessment of a key indicator system to monitor desertification vulnerability

A number of studies have dealt with the assessment of potential and actual desertification risk using composite indices. The Environmental Sensitivity Areas (ESA) approach, developed in the framework of MEDALUS project funded by the European Community, is one of the most used procedures to monitor land vulnerability to degradation in the Mediterranean region. The final output of this procedure is an index (ESI) composing four indicators of climate, soil, vegetation, and land management based on 14 elementary variables. Although applied to a number of case studies throughout southern Europe, northern Africa and the Middle East, the performance of this monitoring system has never been assessed. The present study evaluates the robustness of the ESI through an original procedure incorporating sensitivity analysis and data cost analysis. For each variable, the standard error of the estimate, the correlation coefficient with the ESI, the sensitivity score, and the estimated costs of data collection and handling were calculated in order to evaluate the stability of the final index and the relative importance of each composing variable. The overall performance of the ESI was computed by averaging the score of the four indicators. Variables such as vegetation cover, climate aridity, rainfall, and the degree of land protection provided the largest contribution to the ESI. The illustrated approach is suited to evaluate the overall performance of a set of variables composing a synthetic index. Moreover, to our knowledge, this is the first attempt to consider explicitly the monetary costs of data collection and handling within a composite index evaluation procedure.     

How does forest landscape structure explain tree species richness in a Mediterranean context?

Although the strong relationship between vegetation and climatic factors is widely accepted, other landscape composition and configuration characteristics could be significantly related with vegetation diversity patterns at different scales. Variation partitioning was conducted in order to analyse to what degree forest landscape structure, compared to other spatial and environmental factors, explained forest tree species richness in 278 UTM 10 × 10 km cells in the Mediterranean region of Catalonia (NE Spain). Tree species richness variation was decomposed through linear regression into three groups of explanatory variables: forest landscape (composition and configuration), environmental (topography and climate) and spatial variables. Additionally, the forest landscape characteristics which significantly contributed to explain richness variation were identified through a multiple regression model. About 60% of tree species richness variation was explained by the whole set of variables, while their joint effects explained nearly 28%. Forest landscape variables were those with a greater pure explanatory power for tree species richness (about 15% of total variation), much larger than the pure effect of environmental or spatial variables (about 2% each). Forest canopy cover, forest area and land cover diversity were the most significant composition variables in the regression model. Landscape configuration metrics had a minor effect on forest tree species richness, with the exception of some shape complexity indices, as indicators of land use intensity and edge effects. Our results highlight the importance of considering the forest landscape structure in order to understand the distribution of vegetation diversity in strongly human-modified regions like the Mediterranean.