High-use movement pathways and habitat selection by ungulates

The cumulative movements of large mammals are expressed in many areas as semi-permanent wildlife trails. The mapping of semi-permanent trail networks offers a direct approach to assess habitat selection of high-use movement routes at relatively fine spatial scales across a landscape. Here we examine an ungulate trail network in north-central Utah created and maintained by the repeated movements of mule deer (Odocoileus hemionus) and elk (Cervus elaphus). In a resource selection analysis using multivariable spatial regression analysis, we show that at a spatial scale of 70 m open and low cover and distance to water are important predictors of movement pathway density. We also demonstrate at a scale of 10 m that elk and deer movement pathways are less steep than adjacent terrain. The mapping of trail networks should be a particularly useful technique for examining functional connectivity among resource patches across a landscape and identifying important high-use movement routes.     

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.     

Landscape nutritional patterns and cattle distribution in rangeland pastures

On rangelands, uneven or unmanaged livestock distribution can adversely affect plant community composition, riparian function, or displace wildlife. These issues have historic precedents and are still a challenge for those managing rangelands. A thorough understanding of the mechanisms governing livestock distribution can help land and livestock managers avoid or ameliorate many deleterious effects. To that end, this research tested hypotheses that grazing cattle seek nutritionally superior portions of rangeland pastures. Global positioning system (GPS) collars were used to track cattle movement and activity in three, 800+ ha pastures where the spatial distribution of standing crop, crude protein (CP), neutral detergent fiber (NDF), acid detergent fiber (ADF), and forage digestibility (in situ dry matter disappearance (ISDMD)) were mapped in late spring. Four of five analyses implied grazing cattle spatially responded to forage quantity/quality attributes. Analyses indicated cattle favored higher than average CP (P = 0.006) and ISDMD (P = 0.078), and lower than average NDF (P = 0.003) and standing crop (P = 0.069) locales. No significant effect (P = 0.954) occurred with ADF analyses. Correlations among those variables imply cattle may simultaneously respond to more than one nutritional attribute as they select foraging locales. Stepwise regression, however, relating grazing distribution to geophysical and forage quantity/quality characteristics were extremely poor predictors of where cattle grazed. Listed in order of entry, the model implied elevation above or below stock water, horizontal distance to stock water, forage CP content, and degree of slope were the site specific attributes most associated with cattle distribution. We speculate that cattle interactions with landscape level nutritional dynamics may at least partially explain seasonal changes in distribution and forage use by cattle across the landscape. These findings should help land and livestock managers understand, explain, and manipulate livestock distribution on their holdings.     

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 influence of rapid urbanization and land use changes on terrestrial carbon sources/sinks in Guangzhou,China

Complex changes in carbon sources and sinks caused by rapid urbanization have been observed with extensive changes in the quantity, structure, and spatial pattern of land use types. Based on the modified Carnegie-Ames-Stanford Approach model and on gray relational analysis, we analyzed the influence of land use changes on carbon sinks and emissions in Guangzhou from 2000 to 2012. The aim was to identify suitable options for built-up land expansion that would allow for minimal carbon losses. The key results were as follows: (1) Built-up land increased by 118.91% in Guangzhou city over the study period, with this expansion taking the form of concentric circles extending around the old Yuexiu district. (2) Carbon emissions over the period of analysis significantly exceeded carbon sink capabilities. The total carbon sink decreased by 30.02%, from 535.40 × 10³ t to 374.6 × 10³ t. Total carbon emissions increased by 1.89 times, from 13.73 × 10⁶ t to 39.67 × 10⁶ t; 80% of carbon emissions were derived from energy consumption. (3) There were large differences in the extent of carbon sink losses at different scales of built-up land expansion and land use change. In Guangzhou, the loss of carbon sink is small when cultivated land (though not prime farmland) and water bodies are converted to built-up land on a small scale. The loss of carbon sink is much smaller when grasslands are converted to built-up land on a large scale. However, forested land, which has excellent carbon sink functions, should not be converted. (4) Changes in carbon sinks were mainly affected by natural factors and land urbanization. Changes in carbon emissions were mainly affected by population urbanization, economic urbanization, and land urbanization. (5) To achieve “economical and intensive use of land”, “urban growth boundary” and “ecological red lines” should be determined for government policies on land use management. These factors have great significance for “increasing carbon sinks and reducing carbon emissions” in urban ecological systems.     

Scale matters: Indicators of ecological health along the urban–rural interface near Columbus,Georgia

Ecological data obtained from field plots can provide detailed information about ecosystem structure and function. However, this information typically reflects processes that occur over small spatial areas. Accordingly, it is difficult to extrapolate these data to patterns and processes that take place at regional scales. Satellite imagery can provide a means to explore environmental variables over a larger area. Therefore, our main objective was to examine the utility of a regional ecological assessment tool using landscape indicators of ecosystem health in a rapidly developing area of West Georgia near the city of Columbus. Indicator variables included in the assessment were: population density and change, road density, percent forest land-cover, forest patch density, landscape Shannon's Diversity Index, proportion of all streams with roads within 30 m, proportion of area that has agriculture on slopes >3%, proportion of all streams with adjacent agriculture, and proportion of all streams with adjacent forest cover. Cluster analysis was used to combine these variables into different groups, and resulting cluster means were used to rank regional areas according to degree of environmental impact. To assess the spatial accuracy of this tool results were compared to those obtained from a separate plot-level field-based forest condition study. Results derived using the landscape ecological assessment tool suggest that rural areas were the least environmentally impacted (or most healthy) of all areas in West Georgia, and support the findings from the field study. Results for developing areas were mixed between the two different studies and may be attributed to differences in scale. Overall, it appears that this tool is useful for broad generalizations about a given landscape, but is not detailed enough for site-specific management goals due to its inherent coarse spatial resolution (30 m × 30 m). However, these site-specific goals may be achieved using higher resolution (1 m × 1 m) satellite imagery and warrants further research. In any case, this tool is a useful asset for anyone needing a rapid diagnosis of ecosystem health in an inexpensive and timely manner.     

Relationship of fish and macroinvertebrate communities in the mid-Atlantic uplands: Implications for integrated assessments

Indices developed for stream bioassessment are typically based on either fish or macroinvertebrate assemblages. These indices consist of metrics which subsume attributes of various species into aggregate measures reflecting community-level ecological responses to disturbance. However, little is known about the relationship between fish and macroinvertebrate metrics, or about how ecological health assessments are affected by assemblage-specific responses to disturbance. We used principal component analysis (PCA) and regression analysis of existing fish (n = 371) and macroinvertebrate (n = 442) stream bioassessment data from a multi-source dataset to determine broad scale, within-assemblage metric patterns, and to examine the intercorrelation of fish and macroinvertebrate metrics (n = 246) and their response to watershed area and land use/land cover gradients. Fish and macroinvertebrate metrics expressed as principal components (PCs) accounted for 72.4 and 85.4% of dataset variance, respectively, with PC-metric patterns reflecting aspects of stream impairment including water and habitat quality. Model components predicting fish metric response differed among fish PCs, with watershed area and macroinvertebrate metric response strongly correlated with the first fish PC, and remaining fish PC models consisting of watershed area, land use, and macroinvertebrate PCs. Correlation between fish and macroinvertebrate PCs, and models relating fish and macroinvertebrate PCs generally explained less variation (13–27%) than metric response models of fish (25–34%) and macroinvertebrates (8–38%) to watershed area and land use/land cover variables. Best-response models integrating fish and macroinvertebrate PCs, watershed area, and land use/land cover variables accounted for the greatest variation in fish PCs (32–50%) across sites. Because fish and macroinvertebrate metrics provide different information on ecological condition, integrated use of information from multiple groups may be appropriate when developing monitoring programs.     

Loss of aboveground forest biomass and landscape biomass variability in Missouri,US

Disturbance regimes and forests have changed over time in the eastern United States. We examined effects of historical disturbance (circa 1813 to 1850) compared to current disturbance (circa 2004 to 2008) on aboveground, live tree biomass (for trees with diameters ≥13 cm) and landscape variation of biomass in forests of the Ozarks and Plains landscapes in Missouri, USA. We simulated 10,000 one-hectare plots using random diameters generated from parameters of diameter distributions limited to diameters ≥13 cm and random densities generated from density estimates. Area-weighted mean biomass density (Mg/ha) for historical forests averaged 116 Mg/ha, ranging from 54 Mg/ha to 357 Mg/ha by small scale ecological subsections within Missouri landscapes. Area-weighted mean biomass density for current forests averaged 82 Mg/ha, ranging from 66 Mg/ha to 144 Mg/ha by ecological subsection for currently forested land. Biomass density of current forest was greater than historical biomass density for only 2 of 23 ecological subsections. Current carbon sequestration of 292 TgC on 7 million ha of forested land is less than half of the estimated historical total carbon sequestration of 693 TgC on 12 million ha. Cumulative tree cutting disturbances over time have produced forests that have less aboveground tree biomass and are uniform in biomass compared to estimates of historical biomass, which varied across Missouri landscapes. With continued relatively low rates of forest disturbance, current biomass per ha will likely increase to historical levels as the most competitive trees become larger in size and mean number of trees per ha decreases due to competition and self-thinning. Restoration of large diameter structure and forested extent of upland woodlands and floodplain forests could fulfill multiple conservation objectives, including carbon sequestration.     

Soil organic carbon fractions are affected by different land uses in an agro-pastoral transitional zone in Northeastern China

Soil organic carbon (SOC) consists of various C fractions with different stabilities and chemical compositions that are differently affected by changes in land use. A better understanding of the responses of different C fractions to land uses is vital for maintaining soil quality and mitigating global warming. Using data from a short-term land use experiment in northeastern China, this paper investigated the effect of five land uses, corn cropland (Corn), alfalfa grassland (Alfalfa), artificial grassland of Lyemus chinensis (AG), Lyemus chinensis grassland for mowing (AG + Mow) and restored grassland (RG), on the dynamics of total SOC and four SOC fractions with increasing degrees of oxidizability at 0 to 50 cm depths. The results show that land use had a significant effect (P < 0.05) on the total SOC and SOC fractions of very labile C (F1), labile C (F2) and less labile C (F3), while the difference in recalcitrant C (F4) was less pronounced. SOC in the study area was characterized by a predominantly very labile C fraction, and the percentages of F1 to total SOC were more than 40% for all land uses. Compared with Corn, the treatments AG + Mow, AG and RG decreased the percentage of F1 to SOC (by 4.49%, 6.53% and 3.55%, respectively) and increased the percentages of F2 (by 3.32%, 2.77% and 6.60%, respectively) and F3 (by 4.47%, 3.46% and 0.3%, respectively) to SOC. These findings suggest that land-use type is a major factor that influences soil C fractions and that labile C fractions contribute a large part of the total SOC. In addition, grassland colonization of croplands improves soil C sequestration in northeastern China.     

Beef cattle pasture to wetland reconversion: Impact on soil organic carbon and phosphorus dynamics

There is a major need to understand the historical condition and chemical/biological functions of the ecosystems following a conversion of wetlands to agricultural functions. To better understand the dynamics of soil total organic carbon (TOC) and phosphorus (P) during beef cattle pastures to wetland reconversion, soil core samples were collected from the beef cattle pasture and from the natural wetland at Plant City, FL, during five summer seasons (2002–2007). The levels of TOC and soil P were significantly affected by changing land use and hydrology. Draining natural wetlands to grazed pastures resulted in very pronounced reduction of TOC from 180.1 to 5.4 g g^?1. Cumulative concentrations of total phosphorus (TP) in soils (1134 mg kg^?1) under drained condition are two to three times lower than those in soils (2752 mg kg^?1) under flooded condition over the periods of land use reconversion. There was a declining trend (r = 0.82**; p ≤ 0.01) in total soil P from natural wetland (763 mg kg^?1) to altered pastures (340 mg kg^?1), largely as organic-bound P (natural wetland, 48%; grazed pastures, 44%; altered pastures, 29%). These results are important in establishing baseline information on soil properties in pasture and wetland prior to restoring and reconverting pasture back to wetland conditions. The results further suggest that changes in soil properties due to changing land use and hydrologic conditions (drying and re-wetting) could be long lasting.     

Nationwide analysis on the impact of socioeconomic land use factors and incidence of urothelial carcinoma

BackgroundIncidence rates for urothelial carcinoma (UC) have been reported to differ between countries within the European Union (EU). Besides occupational exposure to chemicals, other substances such as tobacco and nitrite in groundwater have been identified as risk factors for UC. We investigated if regional differences in UC incidence rates are associated with agricultural, industrial and residential land use.MethodsNewly diagnosed cases of UC between 2003 and 2010 were included. Information within 364 administrative districts of Germany from 2004 for land use factors were obtained and calculated as a proportion of the total area of the respective administrative district and as a smoothed proportion. Furthermore, information on smoking habits was included in our analysis. Kulldorff spatial clustering was used to detect different clusters. A negative binomial model was used to test the spatial association between UC incidence as a ratio of observed versus expected incidence rates, land use and smoking habits.ResultsWe identified 437,847,834 person years with 171,086 cases of UC. Cluster analysis revealed areas with higher incidence of UC than others (p = 0.0002). Multivariate analysis including significant pairwise interactions showed that the environmental factors were independently associated with UC (p < 0.001). The RR was 1.066 (95% CI 1.052–1.080), 1.066 (95% CI 1.042–1.089) and 1.067 (95% CI 1.045–1.093) for agricultural, industrial and residential areas, respectively, and 0.996 (95% CI 0.869–0.999) for the proportion of never smokers.ConclusionThis study displays regional differences in incidence of UC in Germany. Additionally, results suggest that socioeconomic factors based on agricultural, industrial and residential land use may be associated with UC incidence rates.     

A multivariate analysis integrating ecological,socioeconomic and physical characteristics to investigate urban forest cover and plant diversity in Beijing,China

Understanding the factors driving the variation in urban green space and plant communities in heterogeneous urban landscapes is crucial for maintaining biodiversity and important ecosystem services. In this study, we used a combination of field surveys, remote sensing, census data and spatial analysis to investigate the interrelationships among geographical and social-economic variables across 328 different urban structural units (USUs) and how they may influence the distributions of urban forest cover, plant diversity and abundance, within the central urban area of Beijing, China. We found that the urban green space coverage varied substantially across different types of USUs, with higher in agricultural lands (N = 15), parks (N = 46) and lowest in utility zones (N = 36). The amount of urban green space within USUs declines exponentially with the distance to urban center. Our study suggested that geographical, social and economic factors were closely related with each other in urban ecological systems, and have important impacts on urban forest coverage and abundance. The percentage of forest as well as high and low density urban areas were mainly responsible for variations in the data across all USUs and all land use/land cover types, and thus are important constituents and ecological indicators for understanding and modeling urban environment. Herb richness is more strongly correlated with tree and shrub density than with tree and shrub richness (r = −0.472, p < 0.05). However, other geographic and socioeconomic factors showed no significant relationships with urban plant diversity or abundance.     

Spatial modeling of deforestation processes in the Central Peruvian Amazon

This study examines the relation between primary forest loss and landscape characteristics in the Ucayali region, Peru. Seven variables (rivers, elevation, annual precipitation, soil suitability for agriculture, population density, paved roads, and unpaved roads), were identified as potential deforestation drivers. The variables were converted into spatially explicit layers of continuous data and divided into a 9 km² grid. A multiple regression analysis was conducted to determine variable significance. Distance to paved and unpaved roads were strongly associated with deforestation, followed by distance to rivers, annual precipitation and elevation. All significant variables were negatively correlated with deforestation. Variables excluded from the model were population density and soil suitability for agriculture, suggesting that the influence of population density on forest clearing across the study area was not significant, and that deforestation activities were undertaken regardless whether soils are suitable for agriculture or not. Based on the linear regression analysis, the significant variables were selected and added to the Land Change Modeler in order to project primary forest coverage by 2025. The modeling results predict extensive deforestation along the Aguaytia River and at the forest/non-forest interface along the paved highway. The rate of primary forest removal is expected to increase from 4783 ha y⁻¹ (for the 2007–2014 period) to 5086 ha y⁻¹ (for the 2015–2025 period). A preliminary survey questionnaire conducted to explore deforestation intentions by farmers in the region, partly confirmed the overall deforestation trends as projected by the model.     

Microhabitat selectivity underpins regional indicators of fish abundance and replenishment

Species with specialized resource use can display strong spatial heterogeneity in abundance according to the availability of their preferred habitats. If these preferences shift with ontogeny, then a wide range of habitats may need to be protected in order to support both adult populations and their replenishment. We explored whether microhabitat selectivity interacts with habitat availability to provide an effective suite of indicators for regional fish abundance and replenishment, using offshore rocky reefs in south-eastern Australia as a case study. We examined generalized additive mixed models (GAMMs) in a full subsets approach to infer the best predictors for adult and juvenile fish density in four diverse families (Labridae, Odacidae, Pomacentridae, Serranidae), based on rapid underwater visual surveys across transects (⿼500 m²), wave exposures (0.3⿿1 km), and sites (0.3⿿48 km). We then examined whether these regional fish-habitat models aligned with the microhabitat electivity of individuals (at scale of <1 m²). Microhabitat selection by reef fishes at the local scale underpinned the most effective habitat indicators for regional heterogeneity in fish abundance, and pointed to critical nursery habitats that support hotspots of juvenile recruitment. Strong species-habitat relationships, such as these, can be combined with broad-scale habitat mapping to assess the potential carrying capacity of focal areas, spatial management zone placements, and nursery habitats that warrant special protection. A number of emerging threats to these key habitat types indicates an urgent need for habitat-based protection and monitoring as a key part of holistic marine ecosystem conservation and management.     

Predictors of non-participation in cervical screening in Denmark

PurposeThe aims of this study were to identify demographic and socio-economic predictors of non-participation in cervical screening in Denmark, and to evaluate the influence of health care use on screening participation.MethodsA population based register study was undertaken using data from the Central Population Register, the national Patobank, and Statistics Denmark. The study included women aged 25–54 years on 1st of January 2002, living in Denmark during the next 5 years, and without a history of total hysterectomy, N = 1,052,447. Independent variables included age, civil status, nationality, level of education, and use of health care. Associations with non-participation in screening were determined with logistic regression.ResultsMain predictors of non-participation were limited or no contact with dental services (odds ratio (OR) = 2.36), general practitioners (OR = 1.75), and high age (OR = 1.98). Other important factors for non-participation were primary school education only (OR = 1.53), not being married (OR = 1.49), and foreign nationality (OR = 1.32).ConclusionA 2–1.5-fold difference in non-participation in cervical screening in Denmark was found across various population sub-groups. Increased screening compliance among women with primary school education only, and limited or no use of primary health care services in general could potentially diminish the current social inequalities in cervical cancer incidence, and thus decrease the overall high incidence of this disease in Denmark.     

Spatial indicators of fire risk in the arid and semi-arid zone of Australia

Fire plays a role in determining the shape of the earth's ecosystems, impacts socio-economic issues, and influences our climate. In arid and semi-arid Australia (70% of the continent), individual fires frequently exceed 1 million ha, and have collectively burnt up to 9% of the total area in a single year, associated with antecedent periods of above average rainfall which boost the fuel load. People affected by these fires – Federal and State governments, pastoralists, Aboriginal communities, larger towns, conservation park managers and tourist operators – all have different outlooks and priorities about these phenomena. Little objective information about the fire regime and its drivers has been available for this vast area with its very low population density. A predictive understanding of the spatial and temporal pattern of risk of large uncontrollable fires is needed to promote pro-active management.We present a conceptual framework which serves both to summarise existing knowledge and to reduce the complexity for a quantitative statistical analysis. This conceptual framework contains four main groups of independent variables; biomass, curing, ignition source, and fire weather. For these groups of variables we identified direct data sources or spatial surrogates. To quantify different aspects of the fire regime, interpretation of NOAA-AVHRR satellite imagery was employed, which identifies both fire hotspots (FHS) and fire affected area (FAA). For temporal variables, we present a surface displaying relationships for different combinations of lag/phase. This highlights different patterns for each region, and the most appropriate timeframes to use in modelling.Results of exploratory regression analysis in arid and semi-arid Australia show that the strongest influence is exerted by biomass or fuel load. As this is highly dependent on antecedent rainfall, we can anticipate a strong effect of climate change on the fire regime. The strongest combinations of relationships may be used as spatial indicators in the development of long-lead fire risk models for these areas. This can help improve the timing of pro-active strategies to manage fire, and in the allocation of sparse funds and resources. Our analysis has highlighted regional patterns of fire across different land tenures. Heightened awareness of these patterns may encourage a more cooperative and coordinated approach to fire management amongst stakeholders.     

Mapping soil organic matter in low-relief areas based on land surface diurnal temperature difference and a vegetation index

Accurate estimates of the spatial variability of soil organic matter (SOM) are necessary to properly evaluate soil fertility and soil carbon sequestration potential. In plains and gently undulating terrains, soil spatial variability is not closely related to relief, and thus digital soil mapping (DSM) methods based on soil–landscape relationships often fail in these areas. Therefore, different predictors are needed for DSM in the plains. Time-series remotely sensed data, including thermal imagery and vegetation indices provide possibilities for mapping SOM in such areas. Two low-relief agricultural areas (Peixian County, 28 km × 28 km and Jiangyan County, 38 km × 50 km) in northwest and middle Jiangsu Province, east China, were chosen as case study areas. Land surface diurnal temperature difference (DTD) extracted from moderate resolution imaging spectroradiometer (MODIS) land surface temperature (LST), and soil-adjusted vegetation index (SAVI) at the peak of growing season calculated from Landsat ETM+ image were used as predictors. Regression kriging (RK) with a mixed linear model fitted by residual maximum likelihood (REML) and residuals interpolated by simple kriging (SK) were used to model and map SOM spatial distribution; ordinary kriging (OK) was used as a baseline comparison. The root mean squared error, mean error and mean absolute error calculated from leave-one-out cross-validation were used to assess prediction accuracy. Results showed that the proposed covariates provided added value to the observations. SAVI aggregated to MODIS resolution was able to identify local highs and lows not apparent from the DTD imagery alone. Despite the apparent similarity of the two areas, the spatial structure of residuals from the linear mixed models were quite different; ranges on the order of 3 km in Jiangyan but 16 km in Peixian, and accuracy of best models differed by a factor of two (3.3 g/kg and 6.3 g/kg SOM, respectively). This suggests that time-series remotely sensed data can provide useful auxiliary variable for mapping SOM in low-relief agricultural areas, with three important cautions: (1) image dates must be carefully chosen; (2) vegetation indices should supplement diurnal temperature differences, (3) model structure must be calibrated for each area.     

The link between landscape pattern and vegetation naturalness on a regional scale

The land use and land cover pattern of landscapes are key elements of basic landscape structure; accordingly, this pattern has an important role in landscape management, nature conservation and preservation. In Hungary, the naturalness of the vegetation was surveyed between 2003 and 2006, and the vegetation-based Natural Capital Index (NCI) was calculated for almost the entire area of the country. This field-based database gave us the unique opportunity to analyse the statistical connection between the naturalness of the vegetation and the landscape (land cover) pattern on a regional scale. In our study, we analysed the efficiency of the regional-level CORINE Land Cover (CLC) database for the estimation of the naturalness of the vegetation. This connection was analysed at the country scale using every (2272) Flora Mapping Unit (FMU), or 5.5 × 6.5 km quadrate, of Hungary. We calculated the shape-, edge- and size-related landscape indices for all FMUs on a landscape level (including all CLC patches) and a class level (the land cover polygons were classified according to their land cover characteristics and their level of hemeroby). We determined the Spearman’s correlations to reveal the statistical connections between the landscape metric parameters and the NCI values. All of the investigated area-weighted landscape indices: Main Patch Size, (MPS), Main Fractal Dimension Index, (MFDI), Total Edge (TE), Main Shape Index (MSI) and Number of Shape Characteristic Points (NSCP) on the landscape level showed a significant statistical connection with the NCI, but the sign of its correlation with the NCI contrasted with the findings from previous studies on a larger scale. Our study shows that scale has a strong impact on the sign of the correlation between the naturalness of the vegetation and the landscape structure. On a class level, particularly the shape-related landscape indices of the “Forest and semi-natural areas” showed statistically significant correlations with the NCI. The correlation strongly depended on the method of classification of the CLC polygons. Furthermore, the spatial pattern of the land-cover-type-based CLC polygon categories showed higher correlation values with the NCI than CLC polygon classes, which were categorized according to their hemeroby state. These results show that although the sign of the spatial pattern change in the main land cover classes is scale-dependent, they can be used to estimate the increase or decrease in the naturalness of the vegetation better than the spatial changes of the hemeroby-level-based landscape pattern. We can predict the change in the naturalness of vegetation based on the spatial changes in the land cover pattern.