The extent of edge effects in fragmented landscapes: Insights from satellite measurements of tree cover

Due to deforestation, intact tropical forest areas are increasingly transformed into a mixture of remaining forest patches and human modified areas. These forest fragments suffer from edge effects, which cause changes in ecological and ecosystem processes, undermining habitat quality and the offer of ecosystem services. Even though detailed and long term studies were developed on the topic of edge effects at local scale, understanding edge effect characteristics in fragmented forests on larger scales and finding indicators for its impact is crucial for predicting habitat loss and developing management options. Here we evaluate the spatial and temporal dimensions of edge effects in large areas using remote sensing. First we executed a neighborhood pixel analysis in 11 LANDSAT Tree Cover (LTC) scenes (180 × 185 km each, 8 in the tropics and 3 in temperate forested areas) using tree cover as an indicator of habitat quality and in relation to edge distance. Second, we executed a temporal analysis of LTC in a smaller area in the Brazilian Amazon forest where one larger forest fragment (25,890 ha) became completely fragmented in 5 years. Our results show that for all 11 scenes pixel neighborhood variation of LTC is much higher in the vicinity of forest edges, becoming lower towards the forest interior. This analysis suggests a maximum distance for edge effects and can indicate the location of unaffected core areas. However, LTC patterns in relation to fragment edge distance vary according to the analyzed region, and maximum edge distance may differ according to local conditions. Our temporal analysis illustrates the change in tree cover patterns after 5 years of fragmentation, becoming on average lower close to the edge (between 50 and 100 m). Although it is still unclear which are the main causes of LTC edge variability within and between regions, LANDSAT Tree Cover could be used as an accessible and efficient discriminator of edge and interior forest habitats in fragmented landscapes, and become invaluable for deriving qualitative spatial and temporal information of ecological and ecosystem processes.     

Mapping critical areas for migratory songbirds using a fusion of remote sensing and distributional modeling techniques

Of the 338 species identified as Nearctic-Neotropical migrants occurring in North America, 98.5% have been recorded in Texas. The seasonal migration of these birds is a well-studied natural phenomenon – individuals weighing < 15 g will cross in the Gulf of Mexico approximately 965 km non-stop, completing a total distance of 1900–3200 km over the course of 26–80 h. The physiologically demanding nature of this feat makes the Texas coastline crucial to the success of these species. We used a fusion of multi-spectral remote sensing data and distributional modeling techniques to generate and evaluate predictive maps identifying critical areas for migratory passerines on the Texas coast. Imagery acquired from Landsat 8 OLI, maps provided by United States Geological Survey and the Texas Department of Transportation, and migratory bird occurrence records from the eBird citizen-contributed database were used to build predictive distribution models using three algorithms. Using the AUC to compare model performance, the Random Forest produced the most accurate distribution model, followed by MaxEnt, and Support Vector Machine (0.98, 0.81, and 0.79, respectively). We interpreted, from Boosted Regression Tree analysis, that elevation is the single most influential factor in determining migrant occupancy, with vegetative biomass the least influential predictor. Our approach here allows conservation biologists a more sophisticated approach to identifying critical areas for migratory passerines across large spatial extents in a short amount of time.     

Identifying potential restoration areas of freshwater wetlands in a river delta

Identification of potential restoration areas is significant and important for implementing a sustainable restoration project and maintaining the ecosystem integrity. We established an eco-hydrological approach to identify potential restoration areas of freshwater wetlands that should and can be restored. Our eco-hydrological method identifies potential restoration areas from three dimensions, namely, transverse, longitudinal and vertical directions. Based on transfer matrix analysis between freshwater wetland and other land cover types and bird habitat suitability assessment, we identified the areas that should be restored under the 1989 and 2000 goals were 36,112 ha and 37,230 ha, respectively. Based on hydrological connectivity and balance between ecological water supply (EWS) and ecological water requirements (EWRs), the area can be restored under the 1989 and 2000 goals were 31,165 ha and 33,963 ha, respectively. The approach and results of this study can help in future restoration efforts in the Yellow River Delta and other similar coastal wetlands.     

Comparative regional-scale soil salinity assessment with near-ground apparent electrical conductivity and remote sensing canopy reflectance

Soil salinity is recognized worldwide as a major threat to agriculture, particularly in arid and semi-arid regions. Producers and decision makers need updated and accurate maps of salinity in agronomically and environmentally relevant ranges (i.e., <20 dS m⁻¹, when salinity is measured as electrical conductivity of the saturation extract, EC_e). State-of-the-art approaches for creating accurate EC_e maps beyond field scale (i.e., 1 km²) include: (i) Analysis Of Covariance (ANOCOVA) of near-ground measurements of apparent soil electrical conductivity (EC_a) and (ii) regression modeling of multi-year remote sensing canopy reflectance and other co-variates (e.g., crop type, annual rainfall). This study presents a comparison of the two approaches to establish their viability and utility. The approaches were tested using 22 fields (total 542 ha) located in California’s western San Joaquin Valley. In 2013 EC_a-directed soil sampling resulted in the collection of 267 soil samples across the 22 fields, which were analyzed for EC_e, ranging from 0 to 38.6 dS m⁻¹. The ANOCOVA EC_a-EC_e model returned a coefficient of determination (R²) of 0.87 and root mean square prediction error (RMSPE) of 3.05 dS m⁻¹. For the remote sensing approach seven years (2007–2013) of Landsat 7 reflectance were considered. The remote sensing salinity model had R² = 0.73 and RMSPE = 3.63 dS m⁻¹. The robustness of the models was tested with a leave-one-field-out (lofo) cross-validation to assure maximum independence between training and validation datasets. For the ANOCOVA model, lofo cross-validation provided a range of scenarios in terms of RMSPE. The worst, median, and best fit scenarios provided global cross-validation R² of 0.52, 0.80, and 0.81, respectively. The lofo cross-validation for the remote sensing approach returned a R² of 0.65. The ANOCOVA approach performs particularly well at EC_e values <10 dS m⁻¹, but requires extensive field work. Field work is reduced considerably with the remote sensing approach, but due to the larger errors at low EC_e values, the methodology is less suitable for crop selection, and other practices that require accurate knowledge of salinity variation within a field, making it more useful for assessing trends in salinity across a regional scale. The two models proved to be viable solutions at large spatial scales, with the ANOCOVA approach more appropriate for multiple-field to landscape scales (1–10 km²) and the remote sensing approach best for landscape to regional scales (>10 km²).     

Expanding temporal resolution in landscape transformations: Insights from a landsat-based case study in Southern Chile

Understanding temporal and spatial dimensions of land cover dynamics is a critical factor to link ecosystem transformation to land and environmental management. The trajectory of land cover change is not a simple difference between two conditions, but a continuous process. Therefore, there is a need to integrate multiple time periods to identify slow and rapid transformations over time. We mapped land cover composition and configuration changes using time series of Landsat TM/ETM+ images (1985–2011) in Southern Chile to understand the transformation process of a temperate rainforest relict and biodiversity hotspot. Our analysis builds on 28 Landsat scenes from 1985 to 2011 that have been classified using a random forests approach. Base on the high temporal data set we quantify land cover change and fragmentation indices to fully understand landscape transformation in this area. Our results show a high deforestation process for old growth forest strongest at the beginning of the study period (1985–1986–1998–1999) followed by a progressive slowdown until 2011. Within different study periods deforestation rates were much larger than the average rate over the complete study period (0.65%), with the highest annual deforestation rate of 1.2% in 1998–1999. The deforestation resulted in a low connectivity between native forest patches. Old-growth forest was less fragmented, but was concentrated mainly in two large regions (the Andes and Coastal mountain range) with almost no connection in between. Secondary forest located in more intensively used areas was highly fragmented. Exotic forest plantation areas, one of the most important economic activities in the area, increased sevenfold (from 12,836 to 103,540 ha), especially during the first periods at the expense of shrubland, secondary forest, grassland/arable land and old grown forest. Our analysis underlines the importance of expanding temporal resolution in land cover/use change studies to guide sustainable ecosystem management strategies as increase landscape connectivity and integrate landscape planning to economic activities. The study is highlighting the key role of remote sensing in the sustainable management of human influenced ecosystems.     

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.     

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.     

Applying geographic information systems and remote sensing for water quality assessment of mangrove forest

The study of environmental conditions is one of the most important measures in the field of reforestation. The present study was undertaken to assess the environmental status of the mangrove forest of Alibaug, Maharashtra, India with respect to different sixteen physicochemical parameters of water using Geographical information system (GIS) for rehabilitation, conservation and development of the destructed area of the mangrove forest. The Base map of study area was prepared using topographic map and the remote sensing data of Landsat 7 ETM + for spatial analysis. The distributions of water pollutants were assigned using a GIS approach of Inverse Distance Weighted (IDW). The results showed that the amounts of EC, COD, hardness, O&G, Cl^?, Na⁺, Ca^2 +, Mg^2 +, NO₃^? and PO₄^3? are higher than the normal ranges in mangrove forest due to natural processes and human activity, industrial and domestic wastewater disposal, oil spillage and agricultural runoff which all eventually affect the water quality of mangrove forest of Alibaug. To identify the areas within the normal ranges of 16 studied parameter, suitability map of water was prepared through an integration of 16 suitability maps of the studied parameters. The suitability map of water classified the water to six classes of suitability in order of moderate > moderate to high > low to moderate > high > low suitable. The areas with classes of 1 and 2 were suitable for the protective measures. Classes 3 and 4 were suitable for replantation and restoration of native mangrove species as well as local communities' cooperation in the participatory protection measures. The areas of classes 5 and 0 need to be designed an urgent management and mitigation plan to reduce impact of human activities. The result of the study also proves the use of GIS as a powerful tool in addressing assessment and monitoring programs of the water quality in the mangrove ecosystems.     

Forecasting sagebrush ecosystem components and greater sage-grouse habitat for 2050: Learning from past climate patterns and Landsat imagery to predict the future

Sagebrush (Artemisia spp.) ecosystems constitute the largest single North American shrub ecosystem and provide vital ecological, hydrological, biological, agricultural, and recreational ecosystem services. Disturbances have altered and reduced this ecosystem historically, but climate change may ultimately represent the greatest future risk. Improved ways to quantify, monitor, and predict climate-driven gradual change in this ecosystem is vital to its future management. We examined the annual change of Daymet precipitation (daily gridded climate data) and five remote sensing ecosystem sagebrush vegetation and soil components (bare ground, herbaceous, litter, sagebrush, and shrub) from 1984 to 2011 in southwestern Wyoming. Bare ground displayed an increasing trend in abundance over time, and herbaceous, litter, shrub, and sagebrush showed a decreasing trend. Total precipitation amounts show a downward trend during the same period. We established statistically significant correlations between each sagebrush component and historical precipitation records using a simple least squares linear regression. Using the historical relationship between sagebrush component abundance and precipitation in a linear model, we forecasted the abundance of the sagebrush components in 2050 using Intergovernmental Panel on Climate Change (IPCC) precipitation scenarios A1B and A2. Bare ground was the only component that increased under both future scenarios, with a net increase of 48.98 km² (1.1%) across the study area under the A1B scenario and 41.15 km² (0.9%) under the A2 scenario. The remaining components decreased under both future scenarios: litter had the highest net reductions with 49.82 km² (4.1%) under A1B and 50.8 km² (4.2%) under A2, and herbaceous had the smallest net reductions with 39.95 km² (3.8%) under A1B and 40.59 km² (3.3%) under A2. We applied the 2050 forecast sagebrush component values to contemporary (circa 2006) greater sage-grouse (Centrocercus urophasianus) habitat models to evaluate the effects of potential climate-induced habitat change. Under the 2050 IPCC A1B scenario, 11.6% of currently identified nesting habitat was lost, and 0.002% of new potential habitat was gained, with 4% of summer habitat lost and 0.039% gained. Our results demonstrate the successful ability of remote sensing based sagebrush components, when coupled with precipitation, to forecast future component response using IPCC precipitation scenarios. Our approach also enables future quantification of greater sage-grouse habitat under different precipitation scenarios, and provides additional capability to identify regional precipitation influence on sagebrush component response.     

Spatial ecology and habitat use of giraffe (Giraffa camelopardalis) in South Africa

The lack of long-term studies remains a limiting factor in understanding the home range, spatial ecology and movement of giraffes. We equipped eight giraffes with GPS satellite units and VHF capacity, which were built in to the collars for the remote collection of data on their movements and home ranges over two years on Khamab Kalahari Nature Reserve (KKNR) within the Kalahari region of South Africa. Giraffe numbers in KKNR dropped from 135 individuals to 111 in just five years, revealing the lack of knowledge about their required habitat needs, space use and diet. With over 1000 km² available for roaming within the reserve, habitat selection, principle and preferred food species played a significant role in home range size and overlap between individuals. These giraffes used an average annual home range of 206 km² (20 602 ha) as calculated by a 95% minimum convex polygon (MCP) with a standard deviation core home range calculated by a 50% MCP of 10.1 km² to satisfy their annual needs for survival and reproduction in their preferred vegetation. In the wet, hot season (summer: December–February) when food was abundant, giraffes frequented smaller areas (average 177 km²), while in the dry, cool season (winter: June to August) the mean home range size increased to approximately 245 km². Rainfall influenced spatial distribution since it determined vegetation productivity and leaf phenology. The different seasons influenced giraffe movements, while different vegetation types and season influenced their home range size. Season and food availability also influenced home range overlap between different giraffe herds. Home range overlap occurred when giraffes were forced to roam in overlapping areas during the dryer months when the winter deciduous nature of the majority of the tree species resulted in lower food availability. In winter, the overlap was approximately 31% and in autumn approximately 23%. During the wet and warmer months, overlapping was 15% in summer and 19% in spring, respectively. The percentage of time spent in different vegetation type areas was influenced by the abundance of the principal food species of that plant community. It is thus concluded that the movements of giraffes were primarily influenced by a combination of environmental factors such as season, rainfall and vegetation density.     

Improving and evaluating the soil cover indicator for agricultural land in Canada

A soil cover days (SCD) model has been developed by Agriculture and Agri-Food Canada for use as an agri-environmental indicator to monitor the relationship between agricultural production activities and agri-environmental quality. The SCD indicator integrates information on crops, soils, climate, and field activities to estimate the total equivalent number of days that agricultural soils are covered by crop canopy, crop residue and snow in a given year. Daily cover fractions of plant and residue for a given crop in an ecoregion are simulated using typical crop calendar and field management practices, and the equivalent number of days that soil is covered by snow in winter is derived from long term climate normals. The equivalent SCD for a spatial unit is then derived as the area-weighted sum of different crops and different management practices within the unit. This paper presents the SCD framework, details an assessment of the accuracy of the model and outlines future improvements. Annual snow days derived from 30-year climate normals as used in the model was strongly correlated (excluding mountain areas) with that derived from satellite data (R² = 0.45, n = 48), even though the remote sensing product showed significant temporal and spatial variability. Crop residue fraction estimated by the model was strongly correlated with field data collected over major crop areas and crop types (R² = 0.74, n = 55), and modelled plant cover fraction was well correlated with that derived from remote sensing data (R² = 0.57, n = 57). Large discrepancies were observed for some samples due to deviation of the actual crop calendar from that estimated using climate normals. National map showing the change in the indicator from 1981 to 2011 reveals changes in crop and residue management practices.     

Body-size spectra of biofilm-dwelling protozoa and their seasonal shift in coastal ecosystems

Community-based assessment of protozoa is usually performed at a taxon-dependent resolution. As an inherent ‘taxon-free’ trait, however, body-size spectrum has proved to be a highly informative indicator to summarize the functional structure of a community in both community research and monitoring programs in aquatic ecosystems. To demonstrate the relationships between the taxon-free resolution of protozoan communities and water conditions, the body-size spectra of biofilm-dwelling protozoa and their seasonal shift and environmental drivers were explored based on an annual dataset collected monthly from coastal waters of the Yellow Sea, northern China. Body sizes were calculated in equivalent spherical diameter (ESD). Among a total of 8 body-size ranks, S2 (19–27 μm), S3 (28–36 μm), S4 (37–50 μm) and S5 (53–71 μm) were the top four levels in frequency of occurrence, while rank S1 (13–17 μm), S2 and S4 were the dominant levels in abundance. These dominants showed a clear seasonal succession: S2/S4 (spring) → S2/S4 (summer) → S4 (autumn) → S2 (winter) in frequency of occurrence; S1 (spring) → S4 (summer) → S2 (autumn) → S1 (winter) in abundance. Bootstrapped average analysis showed a clear seasonal shift in body-size spectra of the protozoa during a 1-year cycle, and the best-matching analysis demonstrated that the temporal variations in frequency of occurrence and abundance were significantly correlated with water temperature, pH, dissolved oxygen (DO), alone or in combination with chemical oxygen demand (COD) and nutrients. Thus, the body-size spectra of biofilm-dwelling protozoa were seasonally shaped and might be used as a time and cost efficient bioindicator of water quality in marine ecosystems.     

Dynamic assessment of the value of vegetation carbon fixation and oxygen release services in Qinghai Lake basin

Studies on ecosystem service function have an important significance for analyzing and understanding global warming. With the introduction of geographic information system (GIS) and remote sensing (RS) technologies for the evaluation of ecosystem service function, the scope for analysis has been widening. Increasing number of researchers use these technologies to quantify the value of ecosystem service functions and reveal their spatial-temporal variability. By using the data for the interpretation of five RS images and net primary productivity (NPP) in Qinghai Lake basin, we assessed the value of vegetation carbon fixation and oxygen release services and revealed their dynamic variation in this basin. The result suggested that the average values of vegetation carbon fixation and oxygen release services in Qinghai Lake basin between 1987 and 2010 were spatially distributed in a ring shape around the Qinghai Lake and decreased from southeastern to the north and northwestern regions; the northwestern areas had the lowest value. The vegetation carbon fixation value between 1987 and 2010 was on an average 28.87 × 10⁸ yuan/a in Qinghai Lake basin, whereas the oxygen release value was 64.41 × 10⁸ yuan/a. Alpine meadow ecosystem showed the highest value of vegetation carbon fixation and oxygen release services function in Qinghai Lake basin, with average values of 18.28 × 10⁸ yuan/a and 40.79 × 10⁸ yuan/a, respectively, followed by those of temperate steppe and sparse vegetation. The vegetation carbon fixation and oxygen release values in Qinghai Lake basin gradually increased from 1987 to 2010, with the maximum value in 2010. By the end of 2010, the values increased by 7.19 × 10⁸ yuan and 16.04 × 10⁸ yuan, respectively. The values slightly decreased in barren land, lakeside marsh, river valley swamp, and sandy areas, but increased to different degrees in other ecosystems. Among them, the largest increase was noted in alpine meadow (4.38 × 10⁸ yuan and 9.78 × 10⁸ yuan, respectively), followed by those in temperate steppe with increased values of 1.12 × 10⁸ yuan and 2.49 × 10⁸ yuan, respectively.     

Valuation of the loss of plant-related ecosystem services caused by water stress in the wetland of China’s Yellow River Delta

China’s Yellow River Delta is ecologically important because of its role as an eco-tone between terrestrial and aquatic ecosystems. However, water stress caused by drought or flooding creates ecological risks for this important ecosystem. In this study, we assessed community biodiversity, plant biomass, and the plant total nitrogen, total phosphorus, and potassium contents to quantify the potential loss of ecosystem services value arising from water stress. The annual ecosystem services and function value of the wetlands totaled 3.68 × 10⁸ Yuan, of which biomass production and local climate regulation accounted for 39.4% and 49.5% of the total, respectively. The area with the highest value (>2 Yuan m⁻²) lies along both banks of the downstream reaches of the river, whereas areas with the lowest values (<1.5 Yuan m⁻²) were located on the northern bank, near the Bohai Sea coastline. We defined scenarios based on three levels of water stress: drought, sufficient water, and flooding. The potential annual value loss in the drought scenario was 3.60 × 10⁸ Yuan, versus 2.78 × 10⁸ Yuan in the flooding scenario. The minimum loss (with sufficient water) was 2.06 × 10⁸ Yuan. The wetland’s soil water content should therefore be managed to protect the vegetation and minimize the ecological risks (and associated ecosystem service value losses) caused by water stress. Our approach provides a tool for assessing the potential loss of ecosystem services and functions and for calculating ecological compensation payments for wetland damage.     

Monitoring the ecosystem service provided by dung beetles offers benefits over commonly used biodiversity metrics and a traditional trapping method

Surveying terrestrial invertebrates often requires lethal techniques that can also kill non-target vertebrates. Removing the desirable components of biodiversity is at odds with the philosophy of ecological restoration and biodiversity conservation more generally. Moreover, commonly used metrics generated by such survey approaches (e.g. abundance and species richness) are only indirectly related to the ecosystem services (e.g. pollination) that are often of primary interest. We examined the relationship between rates of dung removal (a direct measure of an ecosystem service) and dung beetle abundance and species richness in a temperate region of New South Wales, Australia, and examined if dung removal in revegetated riparian areas of different ages were trending monotonically toward rates in areas with mature native vegetation. Pellets of pig manure and conventional traps were left at study sites for 48 h to examine the relationships between rates of dung removal and dung beetle abundance and richness. Regressions of abundance and richness with average percent dung removal were positive and significant, demonstrating the potential of the method as a non-lethal proxy. While the dung removal method cannot determine the species responsible, percentage dung removal was more time-efficient, costing 4 min per sample, while abundance and richness cost 13 and 17 min, respectively. Despite variability among replicates of the same habitat type, the trajectory across the restoration gradient showed an increase from sites recently revegetated toward those with mature woody vegetation. We interpreted these results as a positive response of dung beetle activity and an indication of recovery of this ecosystem service. We argue that responses that can be collected efficiently such as dung removal should be used if restorationists have limited resources for data collection and analysis; non-specialists are involved; knowledge of ecosystem function is required, and animal ethics constrain options.     

Factors controlling Zostera marina L. growth in the eastern and western Pacific Ocean: Comparisons between Korea and Oregon,USA

Zostera marina distribution is circum-global and tolerates a wide range of environmental conditions. Consequently, it is likely that populations have adapted to local environmental conditions of light, temperature and nutrient supply. We compared Z. marina growth dynamics over a 2-year period in relation to environmental characters at Jindong Bay, South Korea and Yaquina Bay, Oregon, USA. Water temperature in Jindong Bay showed stronger seasonal variation (summer–winter ΔT = 20 °C) than in Yaquina Bay (summer–winter ΔT < 5 °C). Underwater irradiance in Jindong Bay exhibited a winter maximum, while in Yaquina Bay underwater light exhibited a summer maximum. Integrated annual underwater irradiance during 2003 was 2200 and 1200 mol photons m⁻² year⁻¹ in Korea and Oregon, respectively. Z. marina shoot density, biomass and integrated production were not significantly different between the two study sites. Seasonal Z. marina growth in Jindong Bay appeared to be controlled by temperature and light, while the growth pattern in Yaquina Bay suggested light regulation. Several seagrass parameters were correlated to phosphate concentrations, even though nutrients did not appear limiting. Despite differences in environmental factors, relative growth rates and temporal growth dynamics between study sites, integrated annual leaf production was quite similar at 335 and 353 g DW m⁻² year⁻¹ in the Jindong and Yaquina Bay study sites. We suggest that Z. marina net productivity is acclimated to the local environmental conditions and may be a general characteristic of temperate seagrass populations.     

Environmental determinants of Gulf Menhaden (Brevoortia patronus) oil content in the northern Gulf of Mexico

Gulf Menhaden (Brevoortia patronus) are a species of commercial and ecological importance in the northern Gulf of Mexico, provisioning the second largest fishery by weight, in the United States, and providing critical ecosystem services in the coastal region. The recruitment and productivity dynamics of the stock are influenced by a suite of environmental factors but an understanding of the factors that determine individual variation in oil content (an indicator of an individual’s commercial value to the fishery and its dietary value to predators) has not been well described. In this work I describe the temporal dynamics of oil content and determine the demographic characteristics that provide predictive power to describe annual contrasts. I relate the predicted patterns in oil yield to a suite of seasonal environmental data series including: the magnitude of spring Mississippi River discharge, spring wind vectors, and the preceding winter El Nino conditions. Two uncorrelated (r = 0.06, p = 0.81) population-level predictor variables were identified that have explanatory power to describe temporal patterns in oil content (L kg⁻¹); a weight-at-length power function parameter (a) and the von Bertalanffy asymptotic fork length (L_∞, mm FL): L kg⁻¹ = − 0.158 − 0.026*a − 0.00163*L_∞ (p < 0.05, R² = 0.42). Analysis of the impacts of environmental variables on the oil content of Gulf Menhaden was evaluated comprehensively in a Bayesian framework by transforming the observed oil content information from two sources to a common scale. Parameters relating oil content to spring Mississippi River discharge and the preceding winter (December–February) El Nino Southern Oscillation index resulted in sample distributions from the posterior where zero was outside the 95% credible interval. This work contributes to the understanding of Gulf Menhaden as a prey species in the Gulf of Mexico and indicates that the value of the species to both the fishery and predators exhibits relatively large inter-annual variability controlled, in part, by seasonal environmental conditions.     

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.     

Tillage effects on carbon footprint and ecosystem services of climate regulation in a winter wheat–summer maize cropping system of the North China Plain

Inappropriate farm practices can increase greenhouse gases (GHGs) emissions and reduce soil organic carbon (SOC) sequestration, thereby increasing carbon footprints (CFs), jeopardizing ecosystem services, and affecting climate change. Therefore, the objectives of this study were to assess the effects of different tillage systems on CFs, GHGs emissions, and ecosystem service (ES) values of climate regulation and to identify climate-resilient tillage practices for a winter wheat (Triticum aestivum L.)-summer maize (Zea mays L.) cropping system in the North China Plain (NCP). The experiment was established in 2008 involving no-till with residue retention (NT), rotary tillage with residue incorporation (RT), sub-soiling with residue incorporation (ST), and plow tillage with residue incorporation (PT). The results showed that GHGs emissions from agricultural inputs were 6432.3–6527.3 kg CO₂-eq ha⁻¹ yr⁻¹ during the entire growing season, respectively. The GHGs emission from chemical fertilizers and irrigation accounted for >80% of that from agricultural inputs during the entire growing season. The GHGs emission from agricultural inputs were >2.3 times larger in winter wheat than that in the summer maize season. The CFs at yield-scale during the entire growing season were 0.431, 0.425, 0.427, and 0.427 without and 0.286, 0.364, 0.360, and 0.334 kg CO₂-eq kg⁻¹ yr⁻¹ with SOC sequestration under NT, RT, ST, and PT, respectively. Regardless of SOC sequestration, the CFs of winter wheat was larger than that of summer maize. Agricultural inputs and SOC change contributed mainly to the component of CFs of winter wheat and summer maize. The ES value of climate regulation under NT was ¥159.2, 515.6, and 478.1 ha⁻¹ yr⁻¹ higher than that under RT, ST, and PT during the entire growing season. Therefore, NT could be a preferred “Climate-resilient” technology for lowering CFs and enhancing ecosystem services of climate regulation for the winter wheat–summer maize system in the NCP.     

Using digital cameras for comparative phenological monitoring in an evergreen broad-leaved forest and a seasonal rain forest

Digital cameras have been used in phenological observations for their high accuracy and low labor cost. Most studies successfully use greenness indices derived from digital images for timing the events related to leaf development. However, when timing the leaf senescence events, wide discrepancies between actual and estimated dates are common. In this study, images of three species (two from an evergreen broad-leaved forest and one from a seasonal rain forest) were used to estimate three phenological events of leaf development and senescence. Other than the greenness index, a redness index was also employed. Different annual patterns in color indices developed among the species. The redness index was more accurate when estimating leaf senescence, while the greenness index was more accurate for estimating leaf development events in Acer heptalobum and Machilus bombycina. The absolute differences in estimations of phenological events ranged from − 3 to 1 day, which is more accurate than estimates based on the greenness index only (− 2 to 27 days). With the introduction of the redness index, this technique has been much improved and is possible to be applied to more species. Furthermore, variations of color indices during periods of phenological events were highly related to the climatic factors with a time lag of around 10 days. Because of the ease of use and efficiency (i.e., automatic daily data output), digital cameras are expected to be used in ecosystem process modeling, networks of phenology assessment and validation of the remote sensing results from satellites.