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1.
Biodiversity persistence in non-woody tropical farmlands is poorly explored, and multi-species assessments with robust landscape-scale designs are sparse. Modeled species occupancy in agricultural mosaics is affected by multiple factors including survey methods (convenience-based versus systematic), landscape-scale agriculture-related variables, and extent of remnant habitat. Changes in seasonal crops can additionally alter landscape and habitat conditions thereby influencing species occupancy. We investigated how these factors affect modeled occupancy of 56 resident bird species using a landscape-scale multi-season occupancy framework across 24 intensively cultivated and human-dominated districts in Uttar Pradesh state, north India. Convenience-based roadside observations provided considerable differences in occupancy estimates and associations with remnant habitat and intensity of cultivation relative to systematic transect counts, and appeared to bias results to roadside conditions. Modeled occupancy of only open-area species improved with increasing intensity of cultivation, while remnant habitat improved modeled occupancy of scrubland, wetland and woodland species. Strong seasonal differences in occupancy were apparent for most species across all habitat guilds. Further habitat loss will be most detrimental to resident scrubland, wetland and woodland species. Uttar Pradesh’s agricultural landscape has a high conservation value, but will require a landscape-level approach to maintain the observed high species richness. Obtaining ecological information from unexplored landscapes using robust landscape-scale surveys offers substantial advantages to understand factors affecting species occupancy, and is necessary for efficient conservation planning.  相似文献   

2.
We examine the hypothesis that human activity changes patterns of variance in soil P (Bray-1) concentrations across several spatial scales. We measured soil P concentrations and variability for each of four different land uses at three distinct levels of analysis. Land uses were remnant prairie, lawns, corn fields of cash grain farms, and corn fields of dairies in Dane County, Wisconsin (USA). For each land use type, levels of analysis were sites (an agricultural field, residential lawn or prairie, ranging in size from 100 m2 to approximately 20 ha), 10-m plots within a site, and points within the 10-m diameter plot. The rank of mean soil P concentrations was cash grain > dairy > lawn > prairie. For all land use types, most of the variance was accounted for by site-to-site variation. Among-site variance was higher for human-dominated sites (0.55, 0.15, 0.14 [log (mg/kg)]2 for cash grain, dairy, and lawn sites, respectively) than it was for prairies (0.07 [log (mg/kg)]2). However, prairies had the highest among-plot variation (0.04 [log (mg/kg)]2) compared to other sites (0.01, 0.002, and 0.01 [log (mg/kg)]2 for cash grain, dairy, and lawn sites, respectively). The results indicate that in this watershed, human activity has increased the mean soil P and variance of soil P, and shifted the scale of variance to larger spatial extents. Human impacts on landscape pattern extend to soil properties that affect nutrient flow and eutrophication of surface waters. Because soil P turns over slowly, the legacy of altered soil P patterns may affect freshwaters for centuries.  相似文献   

3.
Estimating the relative importance of habitat loss and fragmentation is necessary to estimate the potential benefits of specific management actions and to ensure that limited conservation resources are used efficiently. However, estimating relative effects is complicated because the two processes are highly correlated. Previous studies have used a wide variety of statistical methods to separate their effects and we speculated that the published results may have been influenced by the methods used. We used simulations to determine whether, under identical conditions, the following 7 methods generate different estimates of relative importance for realistically correlated landscape predictors: residual regression, model or variable selection, averaged coefficients from all supported models, summed Akaike weights, classical variance partitioning, hierarchical variance partitioning, and a multiple regression model with no adjustments for collinearity. We found that different methods generated different rankings of the predictors and that some metrics were strongly biased. Residual regression and variance partitioning were highly biased by correlations among predictors and the bias depended on the direction of a predictor’s effect (positive vs. negative). Our results suggest that many efforts to deal with the correlation between amount and fragmentation may have done more harm than good. If confounding effects are controlled and adequate thought is given to the ecological mechanisms behind modeled predictors, then standardized partial regression coefficients are unbiased estimates of the relative importance of amount and fragmentation, even when predictors are highly correlated.  相似文献   

4.
Mapping urban vegetation is a prerequisite to accurately understanding landscape patterns and ecological services provided by urban vegetation. However, the uncertainties in fine-scale vegetation biodiversity mapping still exist in capturing vegetation functional types efficiently at fine scale. To facilitate the application of fine-scale vegetation spatial configuration used for urban landscape planning and ecosystem service valuation, we present an approach integrating object-based classification with vegetation phenology for fine-scale vegetation functional type mapping in compact city of Beijing, China. The phenological information derived from two WorldView-2 imagery scenes, acquired on 14 September 2012 and 26 November 2012, was used to aid in the classification of tree functional types and grass. Then we further compared the approach to that of using only one WorldView imagery. We found WorldView-2 imagery can be successfully applied to map functional types of urban vegetation with its high spatial resolution and relatively high spectral resolution. The application of the vegetation phenology into classification greatly improved the overall accuracy of classification from 82.3% to 91.1%. In particular, the accuracies of vegetation types was improved by from 10% to 13.26%. The approach integrating vegetation phenology with high-resolution remote sensed images provides an efficient tool to incorporate multi-temporal data into fine-scale urban classification.  相似文献   

5.
Studies at the global scale show that urban greenness is not equally distributed across and within cities. Yet, quantification of urban greening trends in drylands is still lacking. We have modeled urban greenness dynamics and its determinants, using Landsat-based time-series analyses of NDVI and census data, of 23 localities along a dryland climatic gradient in southern Israel, between 1997 and 2019. NDVI trends and their associated temporal changes in local average wages, age of locality, and average annual rainfall were analyzed by exploiting a panel structure and model parameters estimation with fixed effects to control for unobserved differences between localities, and to strengthen causal interpretation. Results show positive NDVI trends in all localities—indicating that urban construction increases greenness in drylands. However, the localities varied greatly in the slopes of their respective linear NDVI trends (0.05 <α < 0.161). Specifically, we found that, ceteris paribus, the differences in average wages between localities is the most important factor in explaining spatio-temporal differences between the respective NDVI of localities. Given the well-known high correlation between individuals’ wages and other characteristic such as education and income (all important determinants of the Socio-Economic classification of local authorities), this leads us to conclude that residents’ economic characteristics are an important predictor of the level of greenness. Harlin’s Granger causality test for wages and NDVI panel data indicate that wage is a Granger cause of urban greenness (p < 0.001)—however, greenness was not found to be the driving force of wage.  相似文献   

6.
Based on the agricultural landscape of the Sebungwe in Zimbabwe, we investigated whether and how the spatial distribution of the African elephant (Loxodonta africana) responded to spatial heterogeneity of vegetation cover based on data of the early 1980s and early 1990s. We also investigated whether and how elephant distribution responded to changes in spatial heterogeneity between the early 1980s and early 1990s. Vegetation cover was estimated from a normalised difference vegetation index (NDVI). Spatial heterogeneity was estimated from a new approach based on the intensity (i.e., the maximum variance exhibited when a spatially distributed landscape property such as vegetation cover is measured with a successively increasing window size or scale) and dominant scale (i.e., the scale or window size at which the intensity is displayed). We used a variogram to quantify the dominant scale (i.e., range) and intensity (i.e., sill) of NDVI based congruent windows (i.e., 3.84 km × 3.84 km in a 61 km × 61 km landscape). The results indicated that elephants consistently responded to the dominant scale of spatial heterogeneity in a unimodal fashion with the peak elephant presence occurring in environments with dominant scales of spatial heterogeneity of around 457–734 m. Both the intensity and dominant scale of spatial heterogeneity predicted 65 and 68% of the variance in elephant presence in the early 1980s and in the early 1990s respectively. Also, changes in the intensity and dominant scale of spatial heterogeneity predicted 61% of the variance in the change in elephant distribution. The results imply that management decisions must take into consideration the influence of the levels of spatial heterogeneity on elephants in order to ensure elephant persistence in agricultural landscapes.  相似文献   

7.
Knowledge on environmental variability and how it is affected by disturbances is crucial for understanding patterns of biodiversity and determining adequate conservation strategies. The aim of this study is to assess environmental variability in patches undergoing post-fire vegetation recovery, identifying trends of change and their relevant drivers. We particularly evaluate: the value of three spectral indices derived from Landsat satellite data [Normalized Burn Ratio (NBR), Normalized Difference Vegetation Index (NDVI) and Wetness Component of the Tasseled Cap Transformation (TCW)] for describing secondary succession; the effectiveness of three metrics (diversity, evenness and richness) as indicators of patch variability; and how thematic resolution can affect the perception of environmental variability patterns. While the system was previously characterised as highly resilient from estimations of vegetation cover, here we noted that more time is required to fully recover pre-fire environmental variability. Using mean diversity as indicator of patch variability, we found similar patterns of temporal change for the three spectral indices (NBR, NDVI and TCW). Analogous conclusions could be drawn for richness and evenness. Patch variability, measured as diversity, showed consistent patterns across thematic resolutions, although values increased with the number of spectral classes. However, when the variance of diversity was plotted against thematic resolution, different scale dependencies were detected for those three spectral indices, yielding a dissimilar perception of patch variability. In general terms, NDVI was the best performing spectral index to assess patterns of vegetation recovery, while TCW was the worst. Finally, burned patches were classified into three classes with similar trends of change in environmental variability, which were strongly related to fire severity, elevation and vegetation type.  相似文献   

8.
Urban trees provide numerous ecosystem goods and services by providing shade, habitat for wildlife, removal of air pollutants and the removal and storage of atmospheric CO2. Carbon removal services provided by Canadian urban trees have previously been assessed using an IPCC 2006 guidelines approach based on the percentage of urban area covered by tree canopy (UTC) for the 2012 time period (Pasher et al., 2014). That work however provided only a single point in time assessment of the national scale UTC and carbon removal services. The research undertaken for this study was a continuation of this earlier work focusing on a 1990 national scale UTC assessment and carbon sequestration estimates for 1990. UTC estimates for 1990 were developed using a point sampling approach with circa 1990 air photos covering a large portion of Canadian urban areas. In total almost 179,000 points were sampled for the 1990 time period, reassessing 83% of the points used for the previous 2012 assessment. Based on the urban area boundary layers for 1991 and 2011, Canada’s urban areas grew by an estimated 6% for this time period. Most of this growth occurred through conversion of agricultural and forested lands to urban. At the national scale the UTC for 1990 was estimated to be 27.6%, as compared to the 2012 UTC estimate of 26.1%, the difference between estimates for the two time periods fell within the uncertainty range. Carbon removal estimates based on the UTC estimates were also very similar for the two dates with 660.2 kt C removed in 1990 and 662.8 kt C removed in 2012. It was noted that urban development in the Prairie regions resulted in an increase in tree cover as compared to the pre-conversion agricultural and natural landscapes and also that in most urban areas across the country UTC increases through time as tree cover matured in newly developed urban areas. These two assessments provide a time series of urban trees for 22 year time period, which will be useful for further studies and analysis.  相似文献   

9.
Protection of rare ecosystems requires information on their abundance and spatial distribution, yet mapping rare ecosystems, particularly those which are fragmented, is a challenge. Use of high spatial resolution satellite imagery is increasing, in part because it may be well-suited for mapping fine-scale components of landscapes. We classified high spatial resolution QuickBird imagery of coastal British Columbia, Canada into late seral forest associations. With an emphasis on rare forest associations, we compared the classification accuracies resulting from contrasting accuracy assessment techniques. We also evaluated the impact of post-classification image smoothing on the quantity and configuration of rare forest associations mapped. Less common associations were generally classified with lower accuracies than more abundant associations, however, accuracies varied depending on the assessment technique used. In particular, ignoring the presence of fine-scale heterogeneity falsely lowered the estimates of map accuracy by approximately 20%. Smoothing, while generally increasing the accuracies of rare forest associations, had a large effect on their predicted spatial extent and configuration. Simply due to smoothing, areal estimates of rare associations differed by as much as 36%, the number of patches decreased by 73% on average, and mean patch size increased by up to 650%. Our findings indicate that routinely used post-classification and map assessment techniques can greatly impact the portrayal of rare and fragmented ecosystems. Further research is needed on the specific challenges of mapping and assessing the accuracy of rare ecosystems in fragmented and heterogeneous landscapes. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.  相似文献   

10.
Ecologists have long recognized the importance of spatial and temporal patterns that characterize heterogeneity in landscapes. However, despite the realization that inferences about ecological phenomena are scale dependent, little attention has been paid to determining appropriate scales of measurement (e.g., plot or grain size) in studies of landscape dynamics or ecosystem change. This paper compares the results from three data sets using several quantitative methods available for characterizing landscape heterogeneity and/or for determining scale of measurement. Methods evaluated include tests of non-randomness, estimation of patch size, spectral analysis, fractals, variance ratio analysis, and correlation analysis. The results showed that no one method provides consistently good estimates of scale. Thus, sampling strategies for landscape studies should be derived from estimates of patch size and/or scale of pattern obtained from more than one of these methods.  相似文献   

11.
Through its control on soil moisture patterns, topography’s role in influencing forest composition is widely recognized. This study addresses shortcomings in traditional moisture indices by employing a water balance approach, incorporating topographic and edaphic variability to assess fine-scale moisture demand and moisture availability. Using GIS and readily available data, evapotranspiration and moisture stress are modeled at a fine spatial scale at two study areas in the US (Ohio and North Carolina). Model results are compared to field-based soil moisture measurements throughout the growing season. A strong topographic pattern of moisture utilization and demand is uncovered, with highest rates of evapotranspiration found on south-facing slopes, followed by ridges, valleys, and north-facing slopes. South-facing slopes and ridges also experience highest moisture deficit. Overall higher rates of evapotranspiration are observed at the Ohio site, though deficit is slightly lower. Based on a comparison between modeled and measured soil moisture, utilization and recharge trends were captured well in terms of both magnitude and timing. Topographically controlled drainage patterns appear to have little influence on soil moisture patterns during the growing season. In addition to its ability to accurately capture patterns of soil moisture in both high-relief and moderate-relief environments, a water balance approach offers numerous advantages over traditional moisture indices. It assesses moisture availability and utilization in absolute terms, using readily available data and widely used GIS software. Results are directly comparable across sites, and although output is created at a fine-scale, the method is applicable for larger geographic areas. Since it incorporates topography, available water capacity, and climatic variables, the model is able to directly assess the potential response of vegetation to climate change.  相似文献   

12.
Land cover data are widely used in ecology as land cover change is a major component of changes affecting ecological systems. Landscape change estimates are characterized by classification errors. Researchers have used error matrices to adjust estimates of areal extent, but estimation of land cover change is more difficult and more challenging, with error in classification being confused with change. We modeled land cover dynamics for a discrete set of habitat states. The approach accounts for state uncertainty to produce unbiased estimates of habitat transition probabilities using ground information to inform error rates. We consider the case when true and observed habitat states are available for the same geographic unit (pixel) and when true and observed states are obtained at one level of resolution, but transition probabilities estimated at a different level of resolution (aggregations of pixels). Simulation results showed a strong bias when estimating transition probabilities if misclassification was not accounted for. Scaling-up does not necessarily decrease the bias and can even increase it. Analyses of land cover data in the Southeast region of the USA showed that land change patterns appeared distorted if misclassification was not accounted for: rate of habitat turnover was artificially increased and habitat composition appeared more homogeneous. Not properly accounting for land cover misclassification can produce misleading inferences about habitat state and dynamics and also misleading predictions about species distributions based on habitat. Our models that explicitly account for state uncertainty should be useful in obtaining more accurate inferences about change from data that include errors.  相似文献   

13.

Context

Invasive Burmese pythons are altering the ecology of southern Florida and their distribution is expanding northward. Understanding their habitat use is an important step in understanding the pathways of the invasion.

Objectives

This study identifies key landscape variables in predicting relative habitat suitability for pythons at the present stage of invasion through presence-only ecological niche modeling using geographical sampling bias correction.

Methods

We used 2014 presence-only observations from the EDDMapS database and three landscape variables to model habitat suitability: fine-scale land cover, home range-level land cover, and distance to open freshwater or wetland. Ten geographical sampling bias correction scenarios based on road presence and sampling effort were evaluated to improve the efficacy of modeling.

Results

The best performing models treated road presence as a binary factor rather than a continuous decrease in sampling effort with distance from roads. Home range-level cover contributed the most to the final prediction, followed by proximity to water and fine-scale land cover. Estuarine habitat and freshwater wetlands were the most important variables to contribute to python habitat suitability at both the home range-level and fine-scale. Suitability was highest within 30 m of open freshwater and wetlands.

Conclusions

This study provides quantifiable, predictive relationships between habitat types and python presence at the current stage of invasion. This knowledge can elucidate future targeted studies of python habitat use and behavior and help inform management efforts. Furthermore, it illustrates how estimates of relative habitat suitability derived from MaxEnt can be improved by both multi-scale perspectives on habitat and consideration of a variety of bias correction scenarios for selecting background points.
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14.
Most of current products can partially reach the requirement of high spatial and temporal resolution needed in urban applications. Fortunately, the new generation of satellite in a form of constellation, e.g. Europe’s Sentinel-2, China’s HJ-1A/B and GF-1/6, is expected to provide more frequent observations (<1 week) with a higher spatial resolution (<30 m). Consequently, a proper method should be selected to construct high spatio-temporal time-series NDVI and to derive phenological features for urban applications. In this study, a high spatio-temporal NDVI product for urban scale vegetation time series is conducted based on HJ-1A/B data. Three related issues, i.e. the optimal filter, time series decomposition, phenological features derivation are addressed. In addition, the effect of spatial and temporal resolution on the phenological features extraction is also discussed according to the comparison between the derived NDVI product and that extracted from MODIS. The results show that the Savitzky-Golay (S-G) filter is the best filter for the reconstruction of HJ NDVI time series. There is some difference for phenology derivation using “season” and “season + trend” depending on the absence/presence of breakpoints in the curve. The spatial details of phenological features can be built by the high-spatial time-series NDVI, showing a great potential in urban applications. Compared with the MODIS NDVI time series, HJ NDVI time series can get more detail information than overall phenological features because of its high spatio-temporal resolution.  相似文献   

15.
The use of NOAA-AVHRR NDVI time series from July 1981 to December 2000 was evaluated for the assessment of the functioning of a wetland macrosystem, the Paraná River Delta. The spatial resolution of the dataset was 8 by 8 km. Spatial and temporal variations in NDVI pattern were analyzed and evidences for El Niño/South Oscillation events identified. We studied five wetland units (WUs) classified on the basis of landscape pattern and dominant hydrologic regime. Spearman rank correlations were performed among the NDVI time series of the different WUs. NDVI time series were correlated with water level in the Paraná River and with records of local rainfall. In order to obtain a synthetic model of NDVI patterns, the autocorrelation functions (ACF) were estimated for each of the WUs. Results indicated that monthly mean NDVI values for all WUs showed a similar annual seasonal pattern, suggesting a control from the plant annual cycle on the NDVI signal. Besides, two general NDVI patterns were identified. The first pattern is represented by WUs under fluvial hydrologic regime. This is subjected to a significative interannual variability associated mainly to ENSO events. The second pattern corresponds to WUs with a very regular NDVI patterns. It includes wetlands which water input corresponds to tides or to rainfall. The ENSO had no significant influence on this pattern. This study suggests that NOAA-AVHRR NDVI long time series might provide valuable information about functioning of the large scale fluvial wetlands like those associated with South America basins.  相似文献   

16.
Estimating landscape resistance to animal movement is the foundation for connectivity modeling, and resource selection functions based on point data are commonly used to empirically estimate resistance. In this study, we used GPS data points acquired at 5-min intervals from radiocollared pumas in southern California to model context-dependent point selection functions. We used mixed-effects conditional logistic regression models that incorporate a paired used/available design to examine the sensitivity of point selection functions to the scale of available habitat and to the behavioral state of individual animals. We compared parameter estimates, model performance, and resistance estimates across 37 scales of available habitat, from 250 to 10,000 m, and two behavioral states, resource use and movement. Point selection functions and resistance estimates were sensitive to the chosen scale of the analysis. Multiple characteristic scales were found across our predictor variables, indicating that pumas in the study area are responding at different scales to different landscape features and that multi-scale models may be more appropriate. Additionally, point selection functions and resistance estimates were sensitive to behavioral state; specifically, pumas engaged in resource use behavior had an opposite selection response to some land cover types than pumas engaged in movement behavior. We recommend examining a continuum of scales and behavioral states when using point selection functions to estimate resistance.  相似文献   

17.
Geostatistical scaling of canopy water content in a California salt marsh   总被引：2，自引：0，他引：2  
Sanderson  E.W.  Zhang  M.  Ustin  S.L.  Rejmankova  E. 《Landscape Ecology》1998,13(2):79-92
Remote sensing data are typically collected at a scale which is larger in both grain and extent than traditional ecological measurements. To compare with remotely sensed data on a one-to-one basis, field measurements frequently must be rescaled to match the grain of image data. Once a one-to-one correspondence is established, it may be possible to extrapolate site based relationships over a wider extent. This paper presents a methodology for rescaling the grain of ecological field data to match the grain of remotely sensed data and gives an example of the method in verification of remote sensing estimates of canopy water content in a tidal salt marsh. We measured canopy water content at 169 points on a semi-regular grid in the Petaluma Marsh, CA. A variogram describing the spatial correlation structure of the canopy water content was calculated and modeled. Ordinary kriging estimates of the canopy water content were calculated over blocks corresponding to image pixels acquired by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). A water content index was determined from the reflectance data by calculating the area of a water absorption feature at 970 nm. A regression developed between the blocks and the pixels at the site was extrapolated over the image to obtain an estimate of canopy water content for the entire marsh. The patterns of canopy water content at the site and landscape levels suggest that different processes are important for determining patterns of canopy water content at different spatial extents. The errors involved in the rescaling procedures and the remote sensing interpretation are discussed.  相似文献   

18.

Many ecological and epidemiological studies occur in systems with mobile individuals and heterogeneous landscapes. Using a simulation model, we show that the accuracy of inferring an underlying biological process from observational data depends on movement and spatial scale of the analysis. As an example, we focused on estimating the relationship between host density and pathogen transmission. Observational data can result in highly biased inference about the underlying process when individuals move among sampling areas. Even without sampling error, the effect of host density on disease transmission is underestimated by approximately 50 % when one in ten hosts move among sampling areas per lifetime. Aggregating data across larger regions causes minimal bias when host movement is low, and results in less biased inference when movement rates are high. However, increasing data aggregation reduces the observed spatial variation, which would lead to the misperception that a spatially targeted control effort may not be very effective. In addition, averaging over the local heterogeneity will result in underestimating the importance of spatial covariates. Minimizing the bias due to movement is not just about choosing the best spatial scale for analysis, but also about reducing the error associated with using the sampling location as a proxy for an individual’s spatial history. This error associated with the exposure covariate can be reduced by choosing sampling regions with less movement, including longitudinal information of individuals’ movements, or reducing the window of exposure by using repeated sampling or younger individuals.

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19.
The percentage of a watershed occupied by agricultural areas is widely used to predict nutrient loadings and in-stream water chemistry because water quality is often linked to non-point sources in a watershed. Measures of the spatial location of source areas have generally not been incorporated into such landscape indicators although empirical evidence and watershed loading models suggest that spatially explicit information is useful for predicting loadings. I created a heuristic grid-based surface-flow model to address the discrepancies between spatially explicit and non-spatial approaches to understanding watershed loading. The mean and variance in loading were compared among thousands of simulated watersheds with varying percentages of randomly located source and sinks. The variability in loading among replicate landscapes was greatest for those landscapes with ~65% source areas. This variance peak suggests that considering the spatial arrangement of cover types is most important for watersheds with intermediate relative abundances of sources and sinks as the wide variety of different spatial configurations can lead to either very high or very low loading. Increasing the output from source pixels (relative to the amount absorbed by sink pixels) among different landscapes moved the peak in variance to landscapes with lower percentages of sources. A final scenario examined both broad- and fine-scale heterogeneity in source output to disentangle the relative contributions of spatial configuration, percentage of source covers, and heterogeneity of sources in governing variability in loading. In landscapes with high percentages of source pixels, fine-scale heterogeneity in source output was responsible for a greater portion of the total variability in loading among different watersheds than was spatial arrangement. These results provide several testable hypotheses for when spatial and non-spatial approaches might be most useful in relating land cover to water chemistry and suggest improvements for the spatial sensitivity analyses of eco-hydrologic watershed models.  相似文献   

20.
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