Indicators of hemeroby for the monitoring of landscapes in Germany

The article discusses the concepts of “closeness to nature” and “hemeroby”, and outlines a method to establish two indicators of hemeroby. Until now Germany's national land use monitoring systems have lacked an indicator to capture the naturalness respectively hemeroby of the landscape. Based on digital spatial data on land use (DLM-DE) and the mapping of potential natural vegetation, these indicators have now been estimated for the whole of Germany and illustrated cartographically. The indicators have been integrated into a land use monitoring system (IOER-Monitor). A hemeroby index that considers all hemeroby classes of a reference area (e.g. administrative unit and regular grid cell) is presented as well as an indicator named “Proportion of certain natural areas”. The results on hemeroby of several time-cuts can be used to estimate the cumulative impact of land use changes on the environmental status.     

Landscape heterogeneity metrics as indicators of bird diversity: Determining the optimal spatial scales in different landscapes

Species distribution models are often used to study the biodiversity of ecosystems. The modelling process uses a number of parameters to predict others, such as the occurrence of determinate species, population size, habitat suitability or biodiversity. It is well known that the heterogeneity of landscapes can lead to changes in species’ abundance and biodiversity. However, landscape metrics depend on maps and spatial scales when it comes to undertaking a GIS analysis.We explored the goodness of fit of several models using the metrics of landscape heterogeneity and altitude as predictors of bird diversity in different landscapes and spatial scales. Two variables were used to describe biodiversity: bird richness and trophic level diversity, both of which were obtained from a breeding bird survey by means of point counts. The relationships between biodiversity and landscape metrics were compared using multiple linear regressions. All of the analyses were repeated for 14 different spatial scales and for cultivated, forest and grassland environments to determine the optimal spatial scale for each landscape typology.Our results revealed that the relationships between species’ richness and landscape heterogeneity using 1:10,000 land cover maps were strongest when working on a spatial scale up to a radius of 125–250 m around the sampled point (circa 4.9–19.6 ha). Furthermore, the correlation between measures of landscape heterogeneity and bird diversity was greater in grasslands than in cultivated or forested areas. The multi-spatial scale approach is useful for (a) assessing the accuracy of surrogates of bird diversity in different landscapes and (b) optimizing spatial model procedures for biodiversity mapping, mainly over extensive areas.     

Modeling landscape condition for biodiversity assessment—Application in temperate North America

Conservation decisions are well supported by predictive spatial models that indicate the relative ecological condition of a given place. The intent of this 90 m pixel landscape condition model is to use nationally available spatial data from the USA, Mexico, and Canada to express assumptions regarding the relative ecological effects of land uses on terrestrial natural communities and species. This approach emphasizes and updateable and transparent design which takes advantage of empirical biodiversity data from the USA to both calibrate and validate the model. Map layers depicting infrastructure, land use, and modified vegetation were each scored for site impact and distance decay, and then combined into one map surface. Field observations from Natural Heritage Programs, each scored for relative ecological condition (in categories A = excellent to D = poor), were used to calibrate distance decay parameters. Some 90,000 observations for at-risk species, invasive plant species, and natural communities were used for model validation. Statistically significant distinctions in ecological condition among validation samples were predicted by the resultant spatial model. Variation in landscape condition was then summarized by regional U.S. Landscape Conservation Cooperatives (LCCs) in terms of areas approximating A–D condition. Montane and desert LCCs scored on average much higher in area approximating “A” and “B” landscape condition, while LCCs with more substantial agricultural and urban footprints scored overwhelmingly within the “D” range of condition. Similar analyses illustrated range-wide scoring of landscape condition for major vegetation types across temperate North America.     

Validating Landsat-based landscape metrics with fine-grained land cover data

ContextModerate-grained data may not always represent landscape structure in adequate detail which could cause misleading results. Certain metrics have been shown to be predictable with changes in scale; however, no studies have verified such predictions using independent fine-grained data.ObjectivesOur objective was to use independently derived land cover datasets to assess relationships between metrics based on fine- and moderate-grained data for a range of analysis extents. We focus on metrics that previous literature has shown to have predictable relationships across scales.MethodsThe study area was located in eastern Connecticut. We compared a 1 m land cover dataset to a 30 m resampled dataset, derived from the 1 m data, as well as two Landsat-based datasets. We examined 11 metrics which included cover areas and patch metrics. Metrics were analyzed using analysis extents ranging from 100 to 1400 m in radius.ResultsThe resampled data had very strong linear relationships to the 1 m data, from which it was derived, for all metrics regardless of the analysis extent size. Landsat-based data had strong correlations for most cover area metrics but had little or no correlation for patch metrics. Increasing analysis areas improved correlations.ConclusionsRelationships between coarse- and fine-grained data tend to be much weaker when comparing independent land cover datasets. Thus, trends across scales that are found by resampling land cover are likely to be unsuitable for predicting the effects of finer-scale elements in the landscape. Nevertheless, coarser data shows promise in predicting fine-grained for cover area metrics provided the analysis area used is sufficiently large.     

Can we measure ecological sustainability? Landscape pattern as an indicator for naturalness and land use intensity at regional,national and European level

European landscapes have been shaped over the centuries by processes related to human land use, which are reflected in regionally distinct landscape patterns. Since landscape pattern has been linked to biodiversity and other ecological values of the landscapes, this paper explores landscape pattern as a tool for ecological sustainability assessments at the regional (Austrian Cultural Landscapes), national (Austria) and European (European Union + Norway, Switzerland) level with focus on agricultural landscapes. A set of landscape metrics served as a basis to assess naturalness and geometrisation of Austrian and European landscapes as a proxy for their sustainability. To achieve an accurate spatially explicit assessment, we applied a spatial reference framework consisting in units that are homogeneous in biophysical and socio-economic contexts, adapted the regional approach for its application at European level, and developed relative sustainability thresholds for the landscape metrics. The analyses revealed that several landscape metrics, particularly the “Number of Shape Characterising Points” showed a high correlation with the degree of naturalness. The sustainability map of Austria based on an ordinal regression model revealed well-known problem regions of ecological sustainability. At the European level, the relative deviation from the average pattern showed clearly the simplification processes in the landscapes. However, a better spatial resolution of land cover data would add to the refinement of pattern analysis in regions and therefore the assessment of sustainability. We recommend the combination of information of different scales for the formulation and implementation of sustainability policies.     

A general framework to describe the alteration of natural tree species composition as an indicator of forest naturalness

The alteration of natural tree species composition is defined as the deviation of the current tree species composition from that of the natural state. It can be used as a measure of human influence on forest vegetation, and thus as an indicator of the naturalness of forest vegetation. The aim of the study was to develop a standard procedure for estimating the alteration of natural tree species composition, to explain factors driving alteration and to examine its significance for susceptibility of forest stands to natural disturbances. The alteration of natural tree species composition was estimated for the Dinaric region (5556 km², Slovenia) by the Robič Index of Dissimilarity (RID), ranging from 0 (completely natural) to 100 (completely altered). The index was calculated on the compartment level (24 ha each on average) with data on current and potential natural forest vegetation. The influence of human activities on tree species alteration was examined by using topographic and accessibility variables. The susceptibility of forest stands to natural disturbances was analysed with data on sanitary felling. In the study area, the natural tree species composition of forest stands is moderately preserved; the average value of RID was 50.05, ranging from 1.76 to 100, and the coefficient of variation was 0.49. The alteration of the natural tree species composition of forest stands is primarily the result of forest management and past land use, conditioned either by topography or accessibility of forests. The degree of alteration of tree species composition decreased along the gradients of rockiness, inclination and elevation. A greater degree of alteration appeared on the slopes of intermediate and south facing aspects than on north facing slopes, and in areas that were closer to the forest edge. A higher level of alteration significantly increases the susceptibility of forest stands to natural disturbances. The procedure represents a novel approach in modelling the alteration (naturalness) of tree species composition of forest vegetation. It is applicable at different spatial scales and fosters an understanding of the patterns of tree species composition under the influence of human activity across forest landscapes.     

Evaluating the performance of multiple remote sensing indices to predict the spatial variability of ecosystem structure and functioning in Patagonian steppes

Assessing the spatial variability of ecosystem structure and functioning is an important step towards developing monitoring systems to detect changes in ecosystem attributes that could be linked to desertification processes in drylands. Methods based on ground-collected soil and plant indicators are being increasingly used for this aim, but they have limitations regarding the extent of the area that can be measured using them. Approaches based on remote sensing data can successfully assess large areas, but it is largely unknown how the different indices that can be derived from such data relate to ground-based indicators of ecosystem health. We tested whether we can predict ecosystem structure and functioning, as measured with a field methodology based on indicators of ecosystem functioning (the landscape function analysis, LFA), over a large area using spectral vegetation indices (VIs), and evaluated which VIs are the best predictors of these ecosystem attributes. For doing this, we assessed the relationship between vegetation attributes (cover and species richness), LFA indices (stability, infiltration and nutrient cycling) and nine VIs obtained from satellite images of the MODIS sensor in 194 sites located across the Patagonian steppe. We found that NDVI was the VI best predictor of ecosystem attributes. This VI showed a significant positive linear relationship with both vegetation basal cover (R² = 0.39) and plant species richness (R² = 0.31). NDVI was also significantly and linearly related to the infiltration and nutrient cycling indices (R² = 0.36 and 0.49, respectively), but the relationship with the stability index was weak (R² = 0.13). Our results indicate that VIs obtained from MODIS, and NDVI in particular, are a suitable tool for estimate the spatial variability of functional and structural ecosystem attributes in the Patagonian steppe at the regional scale.     

Application of surrogate measures of ecological quality assessment: The introduction of the Indicator of Ecological Landscape Quality (IELQ)

Despite the fact that many ecological landscape indicators have been applied so far, it is challenging to develop an indicator(s) which is easy and cheap to assess while at the same time reflecting the complexity of an ecosystem. The author aimed at the development of such an indicator: the Indicator of Ecological Landscape Quality (IELQ) which ranged from 0 to 2. This is based on the use of surrogate measures of assessing ecological quality (EQ) and a GIS approach. The employed measures include: landscape diversity, the degree of ecological significance of land cover forms, and the character of linear landscape structures. To verify the validity of the adopted measures, the indicator was calculated for areas under different conservation statuses and adjoining non-protected areas located in eastern Poland, 20 sites in total. The results showed that differences in mean IELQ values between areas under different protection regimes are significant (Tukey: p < 0.01). The highest mean values were obtained for nature reserves (0.86), and the lowest for non-protected areas (0.27). Explanatory factors for EQ included the proportion of natural and anthropogenic land cover forms, and the number of land cover forms. Surprisingly, the correlation between the latter factor and IELQ is negative (−0.704), indicating that the lower the landscape diversity, the higher the ecological values. From the protected area management point of view, the developed indicator proved its usefulness in terms of an assessment of ecological values and indicated changes in values over time.     

The effect of semi-natural habitats on aphids and their natural enemies across spatial and temporal scales

Semi-natural habitats in agricultural landscapes are generally assumed to enhance the biological control of insect pests based on native beneficial insects, by providing alternative prey and hosts, resources and refuges for overwintering. We hypothesized that natural enemies of winter wheat aphids should arrive sooner in fields near semi-natural habitats. We compared aphid, hoverfly (larvae and eggs) and parasitized aphid (mummies) abundances in 54 winter wheat fields located in southern France from 2003 to 2007. Six surveys were recorded each spring and were split into the early period (defined as the period before the peak of aphid growth) and the late period (after the peak). The wheat fields differed by their surrounding landscape composition measured as the proportion of semi-natural habitats (woods, hedges and grasslands), at three different spatial scales: 200 m, 500 m, and 1200 m. Despite great variability in abundance data between years, the abundance of hoverflies appeared more sensitive to landscape composition than aphid abundance was. Early abundance for both aphids and hoverflies was positively related to wood cover, but not late abundance in spring. The abundance of hoverflies was positively related to hedge and grassland cover at all spatial scales and both periods considered. Aphid parasitism was higher near hedges at the small spatial scale late in the spring. Our results confirmed that higher proportions of semi-natural habitats in agricultural landscapes enhance the biological control of pests, but this effect depends on the spatial scale, the time period in the spring and the natural enemies considered.     

Creating spatially continuous maps of past land cover from point estimates: A new statistical approach applied to pollen data

Reliable estimates of past land cover are critical for assessing potential effects of anthropogenic land-cover changes on past earth surface-climate feedbacks and landscape complexity. Fossil pollen records from lakes and bogs have provided important information on past natural and human-induced vegetation cover. However, those records provide only point estimates of past land cover, and not the spatially continuous maps at regional and sub-continental scales needed for climate modelling.We propose a set of statistical models that create spatially continuous maps of past land cover by combining two data sets: 1) pollen-based point estimates of past land cover (from the REVEALS model) and 2) spatially continuous estimates of past land cover, obtained by combining simulated potential vegetation (from LPJ-GUESS) with an anthropogenic land-cover change scenario (KK10). The proposed models rely on statistical methodology for compositional data and use Gaussian Markov Random Fields to model spatial dependencies in the data.Land-cover reconstructions are presented for three time windows in Europe: 0.05, 0.2, and 6 ka years before present (BP). The models are evaluated through cross-validation, deviance information criteria and by comparing the reconstruction of the 0.05 ka time window to the present-day land-cover data compiled by the European Forest Institute (EFI). For 0.05 ka, the proposed models provide reconstructions that are closer to the EFI data than either the REVEALS- or LPJ-GUESS/KK10-based estimates; thus the statistical combination of the two estimates improves the reconstruction. The reconstruction by the proposed models for 0.2 ka is also good. For 6 ka, however, the large differences between the REVEALS- and LPJ-GUESS/KK10-based estimates reduce the reliability of the proposed models. Possible reasons for the increased differences between REVEALS and LPJ-GUESS/KK10 for older time periods and further improvement of the proposed models are discussed.     

Integrating geo-biodiversity features in the analysis of landscape patterns

Spatial patterns are deeply linked to ecological processes and this relationship lies at the core of landscape ecology. In turn, landscape patterns are influenced by physical, biological and anthropogenic factors. The aim of this study was to explore how specific physical and biological factors, namely geo- and biodiversity features influence landscape patterns. The focus was on microscale relationships and we chose as our focus area a small scale study site covering 3091 ha characterized by vegetation mosaics with multiple patterns. We considered geology, soil and altitude (for geodiversity) and land cover classes (for biodiversity) as superposed layers and we aggregated their elements into a new combined mosaic. Several landscape metrics related to patterns such as landscape fragmentation, connectivity of habitats and ecotone properties were computed at the class level for the new mosaic and were used in multivariate statistical analyses. We determined the most important parameters by Principal Component Analysis. The first component was mainly linked to metrics related to size variability, while the second one was related to border complexity. In the reduced space, we delineated three clusters of objects that were characterized by different landscape patterns. We analyzed the underlying geology, soil structure and occurring land cover classes for each cluster. We then performed Redundancy Analysis using geo- and biodiversity features as predictor variables and metrics as response variables. While the land cover acted as explanatory variable for the first principal axis of variation, the geodiversity features (geology and soil) were related to the second one. Specifically, the occurrence of limestone yields more complex borders of patches; some phenomena are visible in situ, such as limestone appearing at the surface as outcrops (lapis) that induce irregular shapes of the patches. Overall, the analyses hinted that, besides the land cover class, the underlying geology plays an important role in defining landscape patterns, and this relationship can be revealed through the use of appropriate statistical tools. On the other hand, the study area is an agro-silvopastoral landscape, where local traditional management practices are also an important driver for the occurrence of specific patterns. Therefore, understanding the links between geo- and biodiversity characteristics and landscape features can contribute to developing appropriate management and planning strategies.     

Impact of landscape pattern at multiple spatial scales on water quality: A case study in a typical urbanised watershed in China

Buffer zones along rivers and streams can provide water quality services by filtering nutrients, sediment and other contaminants from the surface. Redundancy analysis was used to determine the influence of the landscape pattern at the entire catchment scale and at multiple buffer zone scales (100 m, 300 m, 500 m, 1000 m and 1500 m) on the water quality in a highly urbanised watershed. Change-point analysis was further applied to estimate the specific locations along a gradient of landscape metric that result in a sudden change in the water quality variable. The landscape characteristics for 100 m buffer zones appeared to have a slightly greater influence on the water quality than the entire catchment. The patch density of urban land and the large patch index of water were recognised as the dominant variables influencing the water quality for a 100 m buffer zone. The result of change-point analysis indicated key interval values of the two landscape metrics within the 100 m buffer zone. When the patch density of urban land was >30–40 n/100 ha and the largest patch index of water was >2.5–3.5%, the watershed water quality appeared to be better protected.     

The reliability of a composite biodiversity indicator in predicting bird species richness at different spatial scales

Several biodiversity features can be linked to landscape heterogeneity, that, in turn, can be informative for management and conservation purposes. Usually, the more the landscape is complex the more the biodiversity increases. Biodiversity indicators can be a useful tool to assess biodiversity status, in function of landscape heterogeneity. In this study, we developed a biodiversity indicator, based on Shannon diversity index and built from distribution maps of protected species. With such an approach, we seek to evaluate the feasibility of using a combination of target species as a surrogate for assessing the status of the whole bird community. Our approach was spread over multiple spatial scales, to determine which was the most informative. We selected four species protected by European regulation and generated a presence-absence map from species distribution modelling. We, therefore, used the FRAGSTATS biodiversity metric to calculate Shannon index for the overlapped presence-absence maps, at two spatial scales (500 m and 1000 m). Then, the relationships with the whole community was assessed through generalised least square models, at the spatial scale of 4 ha, 9 ha and 25 ha. Results showed that the higher rate of variability of community was explained by the biodiversity indicator at 1000 m scale. Indeed, the more informative spatial scale for the whole bird community was 9 ha. In addition, a pattern emerged about the relationships between biodiversity indicator and community richness, that is worth of further research. Our study demonstrates that the usefulness of surrogate species for biodiversity and community assessment can become clear only at a certain spatial scales. Indeed, they can be highly predictive of the whole community, and highly informative for conservation planning. Moreover, their use can optimize biodiversity monitoring and conservation, focusing on a small number of noteworthy species.     

近40年艾比湖湿地自然保护区生态干扰度时空动态及景观格局变化

以新疆艾比湖湿地为研究区,利用1972、1998、2007年及2013年4个时期的Landsat遥感影像作为数据源,并结合湿地的土地覆被状况,参考《全国土地利用分类》建立艾比湖湿地生态干扰类型分类系统。借助生态干扰度指数、景观格局指数以及GIS空间分析方法,探讨艾比湖湿地的生态干扰度的时空动态及景观响应机制。结果表明:(1)1972—2013年,研究区的生态干扰度总体呈现较为稳定的趋势,但其空间分布发生变化。生态干扰度类型之间的转化速率有加快的趋势。(2)1972—2013年,边缘密度指数(ED),平均形状指数(MSI),面积加权的平均斑块分形指数(AWMPFD)及景观分离度(DIVISION)4项景观格局指数大体呈上升的趋势,2013年区域的景观指数较为稳定。(3)景观格局指数与生态干扰程度有密切的一致性。生态干扰度与景观格局指数空间分布相关性大小依次为:边缘密度指数(ED)景观丰度密度(PRD)香农多样性指数(SHDI)平均形状指数(MSI)面积加权的平均斑块分形指数(AWMPFD)景观分离度(DIVISION)。客观系统的认识和评价艾比湖湿地的生态系统及环境,可为干旱区实现自然环境的保护,协调土地利用及环境保护之间的关系提供较为实用的参考。     

景观指数耦合景观格局与土壤侵蚀的有效性

景观格局分析是景观生态学中揭示景观变化及其生态效应的主要方法,而景观指数是景观格局分析中广泛使用的工具。土壤侵蚀是土壤物质在景观中的迁移和再分配过程,受地形、植被和人类活动及其空间格局的调控。运用景观格局分析揭示景观格局变化特别是土地利用/覆被格局变化对土壤侵蚀的影响是土壤侵蚀研究中应用景观生态学原理和方法的典型。在当前的研究中,斑块-廊道-基质范式下建立的景观指数对侵蚀过程的解释能力不断受到质疑,建立筛选适用的景观指数的原则和方法十分必要。以延河流域碾庄沟小流域为例,利用WATEM/SEDEM模型模拟多个年份流域侵蚀产沙和输沙量;基于土地利用/覆被数据,利用Fragstat4.2软件,计算了相应年份流域斑块、边界密度、形状、集聚与分散和斑块类型多样性4个方面的代表性景观指数。在此基础上,分析了景观指数与流域侵蚀产沙和输沙之间的关系,讨论了景观指数在土壤侵蚀研究中的有效性,在景观和斑块类型水平上分析了景观指数表达"源"、"汇"两大类景观类型的空间格局与侵蚀产沙和输沙之间的关系的一致性。结果表明:斑块-廊道-基底范式下发展的景观指数在指示景观格局的土壤侵蚀效应时存在局限。相对而言,斑块类型尺度的景观指数更能有效表达景观格局与土壤侵蚀的关系。基于景观类型在土壤侵蚀过程中的"源"、"汇"功能,提出了在土壤侵蚀研究中筛选适用的景观指数的原则:(1)对"源"、"汇"两类景观类型,景观指数与土壤侵蚀状况表征变量的相关系数符号相反;(2)对同为"源"或"汇"景观类型的多个景观类型,景观指数与土壤侵蚀表征变量的相关系数应具有符号一致性。尽管景观指数在斑块类型水平上具有一定的有效性,但用其预测景观格局变化的侵蚀效应有很大的不确定性。因此,基于土壤侵蚀过程与景观格局的作用机制发展新型的景观指数是增强景观格局分析预测土壤侵蚀过程的能力的途径。     

Mapping changes to vegetation pattern in a restoring wetland: Finding pattern metrics that are consistent across spatial scale and time

Tidal salt marshes in the San Francisco Estuary region display heterogeneous vegetation patterns that influence wetland function and provide adequate habitat for native or endangered wildlife. In addition to analyzing the extent of vegetation, monitoring the dynamics of vegetation pattern within restoring wetlands can offer valuable information about the restoration process. Pattern metrics, derived from classified remotely sensed imagery, have been used to measure composition and configuration of patches and landscapes, but they can be unpredictable across scales, and inconsistent across time. We sought to identify pattern metrics that are consistent across spatial scale and time – and thus robust measures of vegetation and habitat configuration – for a restored tidal marsh in the San Francisco Bay, CA, USA. We used high-resolution (20 cm) remotely sensed color infrared imagery to map vegetation pattern over 2 years, and performed a multi-scale analysis of derived vegetation pattern metrics. We looked at the influence on metrics of changes in grain size through resampling and changes in minimum mapping unit (MMU) through smoothing. We examined composition, complexity, connectivity and heterogeneity metrics, focusing on perennial pickleweed (Sarcocornia pacifica), a dominant marsh plant. At our site, pickleweed patches grew larger, more irregularly shaped, and closely spaced over time, while the overall landscape became more diverse. Of the two scale factors examined, grain size was more consistent than MMU in terms of identifying relative change in composition and configuration of wetland marsh vegetation over time. Most metrics exhibited unstable behavior with larger MMUs. With small MMUs, most metrics were consistent across grain sizes, from fine (e.g. 0.16 m²) to relatively large (e.g. 16 m²) pixel sizes. Scale relationships were more variable at the landcover class level than at the landscape level (across all classes). This information may be useful to applied restoration practitioners, and adds to our general understanding of vegetation change in a restoring marsh.     

A new view on EU agricultural landscapes: Quantifying patchiness to assess farmland heterogeneity

Mapping and assessment of ecosystem services in agricultural landscapes as required by the EU biodiversity policy need a better characterization of the given landscape typology according to its ecological and cultural values. Such need should be accommodated by a better discrimination of the landscape characteristics linked to the capacity of providing ecosystem services and socio-cultural benefits. Often, these key variables depend on the degree of farmland heterogeneity and landscape patterns. We employed segmentation and landscape metrics (edge density and image texture respectively), derived from a pan-European multi-temporal and multi-spectral remote sensing dataset, to generate a consistent European indicator of farmland heterogeneity, the Farmland Heterogeneity Indicator (FHI). We mapped five degrees of FHI on a wall-to-wall basis (250 m spatial resolution) over European agricultural landscapes including natural grasslands. Image texture led to a clear improvement of the indicator compared to the pure application of Edge Density, in particular to a better detection of small patches. In addition to deriving a qualitative indicator we attributed an approximate patch size to each class, allowing an indicative assessment of European field sizes. Based on CORINE land cover, we identified pastures and heterogeneous land cover classes as classes with the highest degree of FHI, while agroforestry and olive groves appeared less heterogeneous on average. We performed a verification based on a continental and regional scale, which resulted in general good agreement with independently derived data.     

Effect of woody vegetation at the landscape scale on the abundance of natural enemies in Australian vineyards

Undisturbed vegetation within agricultural areas, especially woody vegetation, has been documented to enhance natural invertebrate enemies within adjacent crops, particularly in northern Europe. To test this idea within the context of Australian vineyards, we considered 44 landscapes from two regions, and sampled invertebrates in vineyards central to each landscape five times at monthly intervals using canopy sticky traps. Landscape composition was characterized at 11 spatial scales from 95 m to 3 km radius. We found only weak relationships between woody vegetation and the abundance of invertebrate groups including coccinellids at any spatial scale, regardless of whether the contribution of each scale was considered independently or together using a multiple regression approach. The only consistent pattern was that several families of parasitoids were influenced by woody vegetation at the landscape scale; the abundance of Eulophidae increased with woody vegetation in the landscape, while two families of smaller parasitoids, Trichogrammatidae and Mymaridae, were negatively affected by woody vegetation. We discuss possible reasons for these apparent contrasting patterns between Australian and European studies.     

Changes and interactions between forest landscape connectivity and burnt area in Spain

The spatial structure, functionality and dynamics of forest landscapes in peninsular Spain and the Balearic Islands were compared over the last five decades. Two particular features were studied in the sample sites: forest connectivity for wildlife and areas burnt by wildfires. 191 Squares, each 4 km × 4 km, were selected from the SISPARES (the monitoring framework designed to evaluate the trends in the structure of Spanish rural landscapes) environmental strata. Aerial photographs from 1956, 1984, 1998 and 2008 were interpreted and 11 land cover categories mapped and checked in the field, using a minimum mapping area of one hectare. The Equivalent Connected Area Index was used to assess forest connectivity over the sampling period. Social and economical factors were assessed using indicators of farm intensiveness. The Spanish forest connectivity has improved in the last five decades although two different trends can be identified: the first 40 years are characterized by positive rates of growth whereas the 10 last years are characterized by their stability. Nevertheless the area of burnt land was higher along the first 25 studied years and decreased significantly over the last decade.Our results show the climate is the main driver in the evolution of forest connectivity and burnt area in the forest landscapes, playing a direct role on forest biomass production and wildfire ignition and propagation, as well as an indirect role by keeping vertical and horizontal forest continuity through the landscape spatial pattern. Social and economic factors are very important drivers as well: Rural population density and farm size average have been tested as good indicators of landscape artificiality, highly correlated to wildfire hazard and forest connectivity.Finally, we have pointed out the evolutionary path followed by SISPARES framework as a tool for monitoring rural landscapes. It emphasises on the requirement of a 30 years time window for building-up reliable dynamic multifunctional model.