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.     

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.     

Airborne hyperspectral data predict Ellenberg indicator values for nutrient and moisture availability in dry grazed grasslands within a local agricultural landscape

Species-based ecological indices, such as Ellenberg indicators, reflect plant habitat preferences and can be used to describe local environment conditions. One disadvantage of using vegetation data as a substitute for environmental data is the fact that extensive floristic sampling can usually only be carried out at a plot scale within limited geographical areas. Remotely sensed data have the potential to provide information on fine-scale vegetation properties over large areas. In the present study, we examine whether airborne hyperspectral remote sensing can be used to predict Ellenberg nutrient (N) and moisture (M) values in plots in dry grazed grasslands within a local agricultural landscape in southern Sweden. We compare the prediction accuracy of three categories of model: (I) models based on predefined vegetation indices (VIs), (II) models based on waveband-selected VIs, and (III) models based on the full set of hyperspectral wavebands. We also identify the optimal combination of wavebands for the prediction of Ellenberg values. The floristic composition of 104 (4 m × 4 m grassland) plots on the Baltic island of Öland was surveyed in the field, and the vascular plant species recorded in the plots were assigned Ellenberg indicator values for N and M. A community-weighted mean value was calculated for N (mN) and M (mM) within each plot. Hyperspectral data were extracted from an 8 m × 8 m pixel window centred on each plot. The relationship between field-observed and predicted mean Ellenberg values was significant for all three categories of prediction models. The performance of the category II and III models was comparable, and they gave lower prediction errors and higher R² values than the category I models for both mN and mM. Visible and near-infrared wavebands were important for the prediction of both mN and mM, and shortwave infrared wavebands were also important for the prediction of mM. We conclude that airborne hyperspectral remote sensing can detect spectral differences in vegetation between grassland plots characterised by different mean Ellenberg N and M values, and that remote sensing technology can potentially be used to survey fine-scale variation in environmental conditions within a local agricultural landscape.     

Procrustes analysis as a tool for land management

Generalized Procrustes analysis (GPA) is a multivariate technique that involves transformations of data matrices to provide optimal comparability. We propose GPA to quantify the concordance among sets of variables that characterize natural, human and productive subsystems. When the land use fits in with the physical support of agricultural production, people's well-being should be evident in a high concordance between the land use and the social conditions. In a situation of instability each set of variables operates in diverse directions resulting in lower resilience and sustainability. Two GPA were performed, between physical support and land use data sets (concordance = 67.4%), and between land use and social conditions data sets (concordance = 65.3%). The interplay between the pair of concordance values constitutes a bi-dimensional index which serves as an ecological indicator. Based on bootstrap confidence interval, the 49 counties of the Pampa Ecoregion, Argentina, were classified in medium, high or low concordance. The lack of concordance is an indicator of imbalances which may contribute to guide environmental management.     

Expanding ecological appropriation approach: Solar space method and a case study in Yangzhou city,East China

In this paper, the authors made an attempt to measure appropriation of ecosystems more reasonably and effectively based on examining the advantages and disadvantages of emergy analysis and ecological footprint. By combining emergy analysis with ecological footprint (EF) in terms of spatial equivalent of energy productivity, a new method of solar space (SS) and its calculating framework was proposed, and some necessary parameters for this method of analysis were calculated. A case study was followed under this calculating framework for Yangzhou city, Jiangsu province, Eastern China, using the data of the year 2000. The main results are as follows: (1) The ecological space in Yangzhou city includes earth surface space and solar space, and their demands were bigger than their supply. In the year of 2000, the earth surface space had an absolute deficit of 2.3063E?02 Ga-ha per capita (global hectares), and the solar space had an absolute deficit of 1.4944E?04 Sa-ha per capita (solar hectares). (2) There was a relative ecological deficit of 3.68% in earth surface space, and that of 154.86% in solar space. (3) Only freshwater areas and built-up areas were in a state of surplus, and there was a relative ecological surplus of 82.66% for freshwater areas, being greater than that for built-up areas. (4) The ranking of absolute ecological deficit in a descending order was as follows: solar space > arable land > pasture > forest > marine area > garden land, while the relative ecological deficit was the following: solar space > pasture land > forest land > arable land > garden land, except marine areas.A qualitative comparison of methodology was made between solar space method and ecological footprint according to their capability to describe the temporal-spatial characteristics of ecosystems appropriation. Moreover, a quantitative comparison was also conducted to show their differences in applicability to the assessment of ecosystems appropriation. It was shown that the calculating results from solar space method were bigger than that from ecological footprint because the former supplemented additional items of appropriation omitted by the later and included some new items of indirect appropriation that were excluded by the later. It is found that the new measure of solar space operates well as an indicator of the city's appropriation to ecosystems.     

Trentepohlia umbrina on Scots pine as a bioindicator of alkaline dust pollution

The present study was driven by the wish to broaden the selection of ecological indicators for estimating the alkaline dust pollution. The abundance of the algal species Trentepohlia umbrina was studied on Pinus sylvestris trunks, an acidic substrate that it normally does not occupy. The study was carried out in northern Estonia in the surroundings of four major limestone quarries, considerable local-level sources of dust pollution. The cover of T. umbrina on tree trunks was clearly higher near the quarries, evidently due to the elevated bark pH, its maximum values reaching ca. 30% (mean of five trees studied per sample plot). The cover of the species decreased steeply at the distance of 800–900 m from the quarries; further than 1000 m from the quarries the maximum cover was already less than 4%, and further than 2000 m less than 1%. The correlation between the cover of T. umbrina and measured bark pH values indicated a steep increase in cover at about pH 3.8–3.9; the cover values varied between 0 and 4% below and between 10 and 31% above that pH. These results confirm that T. umbrina could be used as an indicator species of alkaline dust pollution. The species forms reddish powdery coating on tree trunks, and in case of high abundance can be very conspicuous looking, being easily detectable at field.     

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.     

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.     

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.     

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.     

Tree loss impacts on ecological connectivity: Developing models for assessment

Trees along linear features are important landscape features, and their loss threatens ecological connectivity. Until recently, trees outside of woodlands (TOWs) were largely unmapped however; the development of innovation mapping techniques provides opportunities to understand the distribution of such trees and to apply spatially explicit models to explore the importance of trees for connectivity. In this study, we demonstrate the utility of models when investigating tree loss and impacts on connectivity. Specifically, we investigated the consequences of tree loss due to the removal of roadside trees, a common management response for diseased or damaged trees, on wider landscape functional connectivity. We simulated the loss of roadside trees within six focal areas of the south east of the UK. We used a spatially explicit individual-based modelling platform, RangeShifter, to model the movement of 81 hypothetical actively dispersing woodland breeding species across these agriculturally fragmented landscapes. We investigated the extent to which removal of trees, from roadsides within the wider landscape, affected the total number of successful dispersers in any given year and the number of breeding woodlands that became isolated through time. On average roadside trees accounted for < 2% of land cover, but removing 60% of them (~ 1.2% of land cover) nevertheless decreased the number of successful dispersers by up to 17%. The impact was greatest when roadside trees represented a greater proportion of canopy cover. The study therefore demonstrates that models such as RangeShifter can provide valuable tools for assessing the consequences of losing trees outside of woodlands.     

Performance of the Phytoplankton Index for Lakes (IPLAC): A multimetric phytoplankton index to assess the ecological status of water bodies in France

A new phytoplankton-based index was designed to respond to the Water Framework Directive (WFD) requirements concerning the assessment of lake ecological status. The “Indice Phytoplancton Lacustre” (IPLAC) is a multimetric index, taking into account biomass, abundance and species composition of communities. The first metric is based on the total phytoplankton biomass (MBA), the second on the abundance and taxonomic composition (MCS) of 165 indicator taxa. The IPLAC was developed on 2 independent databases, one for the calibration and the second for the validation of the metrics. The calibration dataset was composed of 255 “lake-years” from 214 distinct lakes sampled between 2005 and 2012. The validation dataset included 173 lake-years in order to confirm the response of the index to the trophic gradient and anthropogenic pressure.The results show that the IPLAC correctly highlights chemical pressure (eutrophication). Especially high Pearson correlations are shown with total phosphorus (r = −0.71, p-value <0.001), chlorophyll-a (r = −0.83, p-value <0.001) and water transparency (r = 0.73, p-value <0.001) which are the main proxies for the trophic level. Corine land cover was used as an indication of the anthropogenic pressure and good correlations are also found with the watershed land use, negatively correlated with agricultural area (r = −0.60, p-value <0.001), population density (r = −0.36, p-value <0.001) and positively with forest area (r = 0.57, p-value <0.001).The index is WFD-compliant and is dedicated to natural lakes and artificial water bodies in metropolitan France, and will be routinely used by the French Ministry of the Environment to assess lake ecological status through the phytoplankton community. However, the results must be carefully interpreted in two cases: reservoirs with large water level fluctuations, and samples that include less than 5 indicator species.     

Above-ground biomass estimation based on NPP time-series − A novel approach for biomass estimation in semi-arid Kazakhstan

Biomass is a sensitive indicator of environmental change and ecological functioning. Quantification of biomass is essential to identify and monitor those areas threatened by degradation and desertification. This is especially important in arid and semi-arid environments. However, robust techniques to monitor carbon stocks over large areas and through time are still missing. The major objective of the presented study is to develop a novel approach for biomass estimation in semi-arid environments using remote-sensing based Net Primary Productivity (NPP) data.The developed methodical concept aims at derivation of above-ground grass and shrub biomass for natural environments. It is based on NPP time-series and plants’ relative growth rates. Fractional cover data provide information about grass and shrub coverage. The developed approach has been applied to three study areas in Kazakhstan, in which field data were collected for validation.Biomass maps were derived that show the spatial distribution of grass and shrub biomass. Validation revealed a moderate correlation (R = 0.68) with field data for grass biomass. For shrub biomass, a high correlation (R = 0.83) is retrieved when fractional cover information from field observations is used.The presented novel approach for biomass estimation is based on remote sensing derived NPP time-series and is thus potentially transferable in space and time. This is a great advantage compared to commonly applied empirical relationships. The presented concept can be adapted to be applied to other vegetation communities. Providing the necessary data about fractional vegetation cover is available, the method will allow for repeated and large-area biomass estimation for natural semi-arid environments as needed for observing changes in biomass and support sustainable land management.     

The impact of forest encroachment after agricultural land abandonment on passerine bird communities: The case of Greece

Agricultural land abandonment is one of the main drivers of land use change, leading to various responses of farmland ecological communities. In an effort to better understand the effect of agricultural land abandonment on passerine bird communities, we sampled 20 randomly selected sites [1 km × 1 km] in remote Greek mountains, reflecting an abandonment gradient, in terms of forest encroachment. We sampled 169 plots using the point count method of fixed distance (47 passerine species), and we investigated bird diversity and community structure turnover along the gradient. We found that grazing intensity has a beneficial effect hampering forest encroachment that follows progressively land abandonment. Habitat composition changes gradually with forests developing at the expense of open meadows and heterogeneous grasslands. Forest encroachment has a significant negative effect on bird diversity and species richness, affecting in particular typical farmland and Mediterranean shrubland species. Birds form five distinct ecological clusters after land abandonment: species mostly found in pinewoods and cavity-dwelling species; species that prefer open forests forest edges or ecotones; species that prefer shrubland or open habitats with scattered woody vegetation; Mediterranean farmland birds that prefer semi-open habitats with hedges and/or woodlots; and, generalist forest-dwelling or shrubland species. We extracted a set of 22 species to represent the above ecological communities, as a new monitoring tool for agricultural land use change and conservation. We suggest that the maintenance of rural mosaics should be included in the priorities of agricultural policy for farmland bird diversity conservation.     

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.     

Definition of the potential treeline in the European Alps and its benefit for sustainability monitoring

Sustainability indicator systems often use administrative entities as a reference, which may cause over- or underestimations of results within topographically different regions. Within the European Alps the highest impacts due to human activities are concentrated below the potential treeline, making these zones comparable to the potentially highly impacted surroundings of the European Alps. An application of the area below the potential treeline as a reference unit for sustainability indicators allows for a more equitable comparison of the European Alps and their surroundings. Therefore, we first developed a method for the identification of the potential treeline in the European Alps. In a second step we tested the zones below the potential treeline as a reference unit for landscape indicators.In order to obtain the position of the potential treeline, initially the highest forest areas within 7 transects throughout the Alps were identified using Corine land cover and a DEM. The correlation among the highest 10% of forest occurring within each transect was then represented by means of a polynomial regression. The resulting 7 polynomial functions were applied to the European Alps within 5 × 5 km raster-cells, thus ascertaining the potential treeline. For testing the zones below the potential treeline as a reference unit for landscape indicators we selected a set of 9 landscape indicators, calculating them for 5936 Alpine municipalities.The potential treeline ascertained is able to represent the real potential treeline at a regional scale. The mean altitudes of the defined potential treeline are 2000 m at the Alpine margin, and 2200–2350 m in the central regions of the European Alps. While in the inner-Alpine regions the actual treeline is on average situated about 350–400 m below the potential treeline, the difference is much smaller in the Alpine fringe. Identifying the difference between the potential and the actual treeline allows for the first time an assessment of the intensity of human impact in formerly forested mountain areas. The statistical analysis of the indicator results revealed strong differences among the results, with the difference increasing from the Alpine margin to inner-Alpine regions. We conclude that indicators referring to municipal areas below the potential treeline allow for a more equitable comparison of topographically different regions. Furthermore, such indicators provide detailed information of those zones within the European Alps that are subject to the highest impact due to human activities, which is of prime importance for local decision-making processes.     

Extinction thresholds and negative responses of Afrotropical ant-following birds to forest cover loss in oil palm and agroforestry landscapes

Afrotropical ant-following birds are vulnerable to forest loss and disturbance, but critical habitat thresholds regarding their abundance and species richness in human-dominated landscapes, including industrial oil palm plantations, have never been assessed. We measured forest cover through Landsat imagery and recorded species richness and relative abundance of 20 ant-following birds in 48 plots of 1-km², covering three landscapes of Southwest Cameroon: Korup National Park, smallholder agroforestry areas (with farms embedded in forest), and an industrial oil palm plantation. We evaluated differences in encounter frequency and species richness among landscapes, and the presence of critical thresholds through enhanced adaptive regression through hinges. All species were detected in Korup National Park and the agroforestry landscape, which had similar forest cover (>85%). Only nine species were found in the oil palm plantation (forest cover = 10.3 ± 3.3%). At the 1-km² scale, the number of species and bird encounters were comparable in agroforests and the protected area: mean species richness ranged from 12.2 ± 0.6 in the park and 12.2 ± 0.6 in the agroforestry matrix to 1.0 ± 0.4 in the industrial oil palm plantation; whereas encounters decreased from 34.4 ± 3.2 to 26.1 ± 2.9 and 1.3 ± 0.4, respectively. Bird encounters decreased linearly with decreasing forest cover, down to an extinction threshold identified at 24% forest cover. Species richness declined linearly by ca. one species per 7.4% forest cover lost. We identified an extinction threshold at 52% forest cover for the most sensitive species (Criniger chloronotus, Dicrurus atripennis, and Neocossyphus poensis). Our results show that substantial proportions of forests are required to sustain complete ant-following bird assemblages in Afrotropical landscapes and confirm the high sensitivity of this bird guild to deforestation after industrial oil palm development. Securing both forest biodiversity and food production in an Afrotropical production landscape may be best attained through a combination of protected areas and wildlife-friendly agroforestry.     

Effects of urban and non-urban land cover on nitrogen and phosphorus runoff to Chesapeake Bay

The aim of this study was to determine the effects of catchment and riparian stream buffer-wide urban and non-urban land cover/land use (LC/LU) on total nitrogen (TN) and total phosphorus (TP) runoff to the Chesapeake Bay. The effects of the composition and configuration of LC/LU patches were explored in particular. A hybrid-statistical-process model, the SPAtially Referenced Regression On Watershed attributes (SPARROW), was calibrated with year 1997 watershed-wide, average annual TN and TP discharges to Chesapeake Bay. Two variables were predicted: (1) yield per unit watershed area and (2) mass delivered to the upper estuary. The 166,534 km² watershed was divided into 2339 catchments averaging 71 km². LC/LU was described using 16 classes applied to both the catchments and also to riparian stream buffers alone. Seven distinct landscape metrics were evaluated. In all, 167 (TN) and 168 (TP) LC/LU class metric combinations were tested in each model calibration run. Runs were made with LC/LU in six fixed riparian buffer widths (31, 62, 125, 250, 500, and 1000 meters (m)) and entire catchments. The significance of the non-point source type (land cover, manure and fertilizer application, and atmospheric deposition) and factors affecting land-to-water delivery (physiographic province and natural or artificial land surfaces) was assessed. The model with a 31 m riparian stream buffer width accounted for the highest variance of mean annual TN (r² = 0.9366) and TP (r² = 0.7503) yield (mass for a specified time normalized by drainage area). TN and TP loadings (mass for a specified time) entering the Chesapeake Bay were estimated to be 1.449 × 10⁸ and 5.367 × 10⁶ kg/yr, respectively. Five of the 167 TN and three of the 168 TP landscape metrics were shown to be significant (p-value ≤ 0.05) either for non-point sources or land-to-water delivery variables. This is the first demonstration of the significance of riparian LC/LU and landscape metrics on water quality simulation in a watershed as large as the Chesapeake Bay. Land cover metrics can therefore be expected to improve the precision of estimated TN and TP annual loadings to the Chesapeake Bay and may also suggest changes in land management that may be beneficial in control of nutrient runoff to the Chesapeake Bay and similar watersheds elsewhere.     

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.