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The accuracy of land cover-based wetland assessments is influenced by landscape extent
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Widespread degradation of wetlands has motivated the development of tools to evaluate wetland condition. The application of field-based tools over large regions can be prohibitively expensive; however, land cover data may provide a surrogate for intensive assessments, enabling rapid and cost-effective evaluation of wetlands throughout whole regions. Our goal was to determine if land cover data could be used to estimate the biotic integrity of wetlands in Alberta's Beaverhills watershed. Biotic integrity was measured using both plant- and bird-based indices of biotic integrity (IBIs) in 45 wetlands. Land cover data were extracted from seven nested landscape extents (100–3,000 m radii) and used to model IBI scores. Strong, significant predictions of IBI scores were achieved using land cover data from every spatial extent, even after factoring out the influence of location to address the spatial autocorrelation of land cover classes. Plant-based IBI scores were best predicted using data from 100 m buffers and bird-based IBI scores were best predicted using data extracted from 500 m buffers. Road cover or density and measures of the proportion of disturbed land were consistent predictors of IBI score, suggesting their universal importance to plant and bird communities. Simplified models using the proportion of undisturbed land were less accurate than more detailed models (reductions in r2 of 0.31–0.32). Regardless of the level of detail in land cover classification, our results emphasize the need to optimize landscape extent for the taxonomic group of interest: an issue that is typically poorly articulated in studies reporting on the development of GIS-based assessment methods. Our results also highlight the need to calibrate models in test areas before scaling up, to ensure predictive accuracy.
KeywordsWetland assessment Regional assessment Buffer Land cover Index of biotic integrity Birds Vegetation North Saskatchewan watershed Canada Spatial autocorrelation
Concern regarding the degradation of wetlands and loss of wetland services has led to the creation of policies aimed at conserving wetlands. A common impediment to wetland policy implementation is the difficulty in evaluating wetlands at the level necessary to inform land use planning. Intensive approaches to wetland assessment that require site visits, such as indices of biotic integrity (IBIs) or rapid assessment methods, are well established and broadly adopted (e.g., Barbour and Yoder 2000). Unfortunately, the cost and time requirements associated with site visits prohibit the application of intensive methods across broad land use management areas (Brooks et al. 2004).
To facilitate land use planning and to provide habitat managers with greater flexibility in wetland assessments, past research has developed GIS-based assessment tools (e.g., Phillips et al. 2005; Mita et al. 2007; Reiss and Brown 2007). GIS-based tools enable the evaluation of wetland condition using airborne or satellite remotely sensed data, eliminating the need for expensive and time consuming site visits. However, these tools are predicated on the assumption that landscape composition and configuration are predictive of biotic integrity at individual wetlands.
Unfortunately, the relationship between surrounding landscape and wetland condition is not always strong (e.g., Tangen et al. 2003) and may vary with a watershed's hydrological transport capacity (Fraterrigo and Downing 2008). In the Aspen Parkland Ecoregion, for example, most wetlands lack stream inputs, and receive relatively little surface run-off (Devito et al. 2005), meaning that the mechanism typically connecting wetlands to uplands is likely less active than in regions with greater surface water run-off and stream inputs. Without strong predictive relationships, GIS-based assessments may provide misleading evaluations of wetland condition. The lack of strong predictive relationships could result because GIS data are incorrect (e.g., out of date); because of time-lags between disturbance in the surrounding landscape and conditions within the wetland (e.g., Findlay and Bourdages 2000); or because natural variability in ecological and hydrological functions mask the response of wetland biota to disturbances. Furthermore, spatial autocorrelation among land cover types may confound any observed relationships between individual land covers and wetland condition, as has been observed in lotic systems (King et al. 2005). Spatial autocorrelation is the property of having a non-random distribution: it is common in nature as environmental variables are frequently clustered or spread over gradients (Legendre 1993). If the distribution of land covers is non-random, an apparent relationship between land cover and wetland condition could be the result of some unmeasured causal factor that determines the distribution of land cover. Although spatial autocorrelation likely presents a problem for any correlation-based study relating ecological condition to surrounding land cover, it is rarely measured (King et al. 2005).
Even if a relationship between surrounding landscape and wetland condition is strong, it is likely to be influenced by the spatial extent (sensu Turner et al. 1989) at which landscape characteristics are considered (Rooney and Bayley 2011). Different taxa interact with their habitat at different spatial extents or functional grain-sizes (Romero et al. 2009). Thus, the extent at which landscape characteristics will be most predictive of biotic integrity will depend on the biotic assemblage used to measure integrity (Levin 1992; Paltto et al. 2006). For example, mobile birds might be expected to interact with, and thus be influenced by, a larger area of land surrounding a wetland than stationary plants, yet both are commonly used as the basis of IBI development. Issues of landscape extent have long been acknowledged in the field of landscape ecology (e.g., Turner et al. 1989; Wu 2004; Buyantuyev and Wu 2007), yet rarely do studies that propose methods of assessing wetlands using remotely sensed data articulate the issue of optimizing landscape extent for the taxon or assemblage of interest. The need to optimize landscape extent for the taxon of interest has been acknowledged in work on lacustrine wetlands (e.g., Brazner et al. 2007), but, to the best of our knowledge, not in the shallow open-water wetlands characteristic of much of central North America. Thus, our primary goal was to articulate the importance of optimizing landscape extent to the successful use of remotely sensed data in regional wetland assessments.
To develop a valid GIS-based wetland assessment tool, it must first be demonstrated that land cover is predictive of biotic integrity at individual wetlands. Biotic integrity can be represented by a quantitative measure like an IBI score (Karr 1991). Two IBIs have been developed and tested for use in shallow open-water marsh wetlands of Alberta, one reliant on the vegetation community and the other on the wetland-dependent songbird and shorebird community (Wilson and Bayley 2012). Thus, our first objective was to determine whether land cover could predict these IBI scores and, if so, to identify the strongest and most significant model for predicting plant- and bird-based IBIs, respectively. If our hypothesis that land cover is capable of predicting vegetation- and bird-based IBI scores is supported, the next issue to address is whether those relationships vary with landscape extent. Our second objective, therefore, was to identify the optimal spatial extent at which land cover predicts IBI scores and to ascertain whether this optimal extent is the same for IBIs based on both vegetation and wetland-dependent songbirds and shorebirds. We hypothesize that, given the differences in mobility between these two taxa, bird-based IBI scores will be best predicted by land cover data extracted from larger landscapes.
We were aware that spatial autocorrelation among land covers might bias our conclusions regarding our first two objectives (King et al. 2005). If the distribution of land covers is non-random, but rather depends on the location of the site in question, we might wrongly conclude that land cover is predictive of biotic integrity when, in fact, it is location or some other underlying characteristic of the environment (i.e., spatial dependency) that is influencing biotic integrity. Thus, we sought to confirm that our conclusions regarding the capacity of land cover data to predict wetland IBI scores and the optimal spatial extent at which such predictions are made were not merely the consequence of the non-random distribution of land covers.
Within the jurisdiction where we work, the percent of undisturbed land within 100 m of the open-water boundary has been proposed as a simple estimate of wetland condition that can be measured remotely. Similar measures have been successfully used as proxies of detailed land cover elsewhere (e.g., Miller et al. 1997; Brooks et al. 2004; Wardrop et al. 2007; Sundell-Turner and Rodewald 2008). Although simpler to obtain and interpret, such proxies may exclude important information pertinent to biotic integrity, introducing additional error into GIS-based assessments. For example, not all forms of disturbance can be expected to affect biotic integrity equally: urban or industrial development would likely have a stronger influence on the biotic condition of a nearby wetland than low-intensity agriculture (Forrest 2010; Rooney and Bayley 2012). Thus, our third objective was to contrast a model that predicts IBI scores from the percent of undisturbed land surrounding each wetland with more sophisticated models, derived from detailed land cover data, divided into 11 distinct land cover classes.
Study area
The 45 wetlands selected for sampling are situated in the Beaverhills watershed of the Aspen Parkland Ecoregion of Alberta, Canada (53.54°N latitude and 113.50°W longitude), which drains into the North Saskatchewan River. This Ecoregion incorporates the transition zone between northern prairie and southern boreal habitats. The region is generally flat and drainage is poor. Climate is temperate with a daily mean temperature of 2.4 °C, with a maximum in July (mean high of 22.2 °C) and a minimum in January (mean low of −19.1 °C) (EC 2011). Precipitation averages 482.7 mm annually with 374.8 mm falling as rain (EC 2011), although there is substantial inter-annual variability. Vegetation transitions from closed aspen forest in the northern part of the Beaverhills watershed to grassland with aspen patches in the south. Wetlands typical of the region are isolated with few surface water inlets or outlets and drainage is primarily via groundwater recharge (Holden 1993).
We used two different IBIs to measure biotic integrity in each wetland. The first was based on vegetation community data, the second on wetland-dependent songbird and shorebird community data (hereafter the bird-based IBI). Sampling followed methods outlined in Rooney and Bayley (2012), and occurred during the summers of 2008 and 2009. In brief, vegetation was sampled from six quadrats deployed within the wet meadow zone of each wetland in August, when peak biomass is expected. The percent cover of each species present was recorded with taxonomy following Moss and Packer (1983) and names updated using the Integrated Taxonomic Information System online database (ITIS 2011). Quadrat results were averaged to yield data on a per wetland basis. For wetland-dependent songbirds and shorebirds, sites were visited three times during the breeding season (May–July). Three sites were visited between sunrise and 10:30 a.m. each day, and site order was rotated so that each site was visited once at sunrise, once at the middle period, and once at the latest period of the morning. On each visit, auditory surveys (8 min, 50 m fixed-radius point counts) were carried out at two locations spaced at least 150 m apart. All target bird species detected by sight or sound were recorded. Identifications followed the American Ornithologist's Union standard (Poole 2005). The two point counts were summed, and the maximum count from the three visits was taken to yield counts on a per wetland basis.
IBI scores were calculated following Wilson and Bayley (2012). From the vegetation community data, we extracted metrics including the FQI score (Miller and Wardrop 2006; Forrest 2010), the relative cover of native perennials, the relative cover of sedge species, and the width of the wet meadow zone. From the wetland-dependent songbird and shorebird community data, we calculated metrics including the richness of temperate migrants, richness of Passeriformes, and the relative abundance of ground nesters, canopy foragers, and omnivores. We measured the Pearson's correlation between the two IBI scores to evaluate the level of agreement between them, using SYSTAT software (SYSTAT 2007).
Land cover
Satellite imagery was collected on 1 September 2009 and consisted of 2.5 m panchromatic and 10 m multispectral SPOT imagery, which was provided by the Alberta Terrestrial Imaging Centre. We classified the imagery into 16 land cover types using the fuzzy k-means unsupervised classification tool in Geomatica Focus (PCI 2007). Classes were first identified through visual assessment of the SPOT image, Google Earth images, and a 1 m 2009 air photo. Manual editing was performed to reclassify incorrectly classified areas on the map. Significant overlap of land covers was observed in 4 of the 16 classes. Each of these four overlapping classes was masked and an unsupervised classification using six classes was conducted on each. Classes were then classified into land covers using the SPOT image, Google Earth images, and 2009 air photo for validation. The land cover classes were then merged where appropriate (i.e., agriculture with agriculture, etc.) to create a final map with 11 land cover classes with 5 m grid resolution (Supplementary Table S1).
Next, land cover patches were converted to polygons in ArcMap (ESRI 2011) and a series of seven nested buffers were created ranging in radius: 100, 300, 500, 1000, 1500, 2000, and 3000 m. These buffers were generated from the perimeter of the open water of each wetland (i.e., between standing water and emergent vegetation). The area of each land cover class within each buffer was calculated using zonal statistics. Absolute areas were converted to proportional data to standardize among wetlands. Road density was calculated as the sum of the length of all linear road features within each buffer divided by the total buffer area. Road features were taken from the Altalis 1:20,000 base feature road vector layer. The proportion of undisturbed land within each buffer was calculated as the sum of the proportion of grass and forest cover.
Spatial autocorrelation
Positive spatial autocorrelation is the property whereby two points located next to each other are more similar than two points located far apart (Legendre 1993) and results from some underlying spatial dependency (sensu Goodchild 1992). We measured spatial autocorrelation in a subset of four of the 11 land cover classes using the Moran's I index, which typically ranges from −1 to +1 with a zero value indicating a random distribution, positive values indicating that a land cover's distribution is clustered (i.e., positive spatial autocorrelation), and negative values indicating a tendency to over-disperse (Moran 1950). We were interested in the forest, agricultural, urban, and roads land cover classes because of their importance in models predicting IBI scores (see "Results" section). Beginning with the land cover map described above, we ran a two pass 3 × 3 majority filter to remove noise and single pixel features. We then converted this filtered map into binary maps for each land cover class of interest and calculated Moran's I index value, Z scores, and p values in ArcMap (ESRI 2011) to test the null hypotheses that each class was randomly distributed across the study area.
We used backwards stepwise general linear modeling (GLMs) with maximum likelihood estimation in SYSTAT (SYSTAT 2007) to identify the best model of the two IBIs using the relative cover of the 11 land cover classes and road density extracted from each buffer width separately. This modeling approach eliminates issues associated with multi-collinearity among proportional land cover data by automatically accounting for simultaneous contributions from multiple predictors. In order to reduce heteroscedasticity and improve the normality of GLM residuals, land cover data was 2/π × arcsine (square-root(x)) transformed (recommended by McCune and Grace (2002) for percent cover data) whereas road density was log(x + 1) transformed prior to analysis. The model tolerance was 1 × 10−12, the probability threshold for a variable to enter or be removed from the model was 0.1, and each GLM was limited to 20 iterations. We also used regression tree modeling in CART (Steinberg and Colla 1997) to confirm the results of GLM, growing the maximal model with least squares regression and pruning with tenfold cross validation to the minimum cost model. Regression tree models were compared based on their r2 values.
We identified the optimal landscape extent as that which yielded the model with the greatest (1) statistical significance (F value, p value); (2) predictive strength (r2 value); and (3) balance between model accuracy and complexity, using Akaike's information criterion as corrected for small sample sizes (the AICc value), which is a method analogous to the optimal zoning approach described by Jelinski and Wu (1996). Of these criteria, we gave the most weight to the last criteria, as adding predictor variables to a model will generally increase its predictive strength, even if only due to random chance. The buffer size with the lowest AICc value, lowest p value, and largest r2 value was considered the optimal landscape extent at which IBI scores should be related to land cover. We then compared the optimal landscape extent for the vegetation-IBI with the optimal extent for the bird-IBI to evaluate whether the two communities were related to land cover at the same spatial extent.
To confirm that spatial autocorrelation was not responsible for the observed relationships between land cover and IBI scores, we factored out the influence of location and re-ran the models. We did this by first regressing UTM Easting and Northing coordinates on IBI scores for both IBIs and saving the residuals. We then used the saved residuals as response variables in place of raw IBI scores and repeated the backwards stepwise GLMs described above. We also re-ran the optimal models identified in the original model selection after factoring out the influence of location in order to parse the total variation in IBI scores into components explained by both location and land cover, components explained exclusively by location, and components explained exclusively by land cover.
We also modeled the proportion of undisturbed land (also 2/π × arcsine(square-root(x)) transformed) as a predictor of IBI scores. Using the criteria outlined above, we compared these simple one-predictor models with the best models identified by our backwards stepwise GLMs.
The vegetation-based IBI scores ranged from 4.4 to 97.4 whereas the bird-based IBI scores ranged from 11.3 to 100.0, indicating that wetlands spanned a gradient from the reference condition to heavily disturbed. The two IBI scores were strongly correlated with each other (Pearson's r = 0.80, p < 0.00001), revealing good agreement in assessment of wetland condition by the two indices.
Results of GLM predicting vegetation-based IBI scores for 45 wetlands using land cover data extracted from a series of seven nested spatial extents
Buffer (m)
F (df)
p value
94.500 − 10000.585 Road density − 47.199 Agricultural − 137.032 Water − 93.900 Urban − 71.244 EM zone
34.9 (5–39)
89.052 − 14.403 Road density − 56.532 Agricultural − 78.365 Urban
44.9 (3–41)
90.656 − 15823.679 Road density − 56.488 Agricultural − 80.747 Urban
40.2 (3–41)
86.533 − 55.789 Agricultural − 99.440 Urban
42.4 (2–42)
87.898 − 56.021 Agricultural − 97.347 Urban
40.3 (2–42)
51.656 − 75.697 Forest − 211.424 Road
39.5 (2–42)
93.465 − 67.336 Agricultural − 102.244 Urban
36.1 (2–42)
Results of GLM predicting wetland dependent song bird-based IBI scores for 45 wetlands using land cover data extracted from a series of seven nested spatial extents
Buffer (m)
F (df)
p value
93.447 − 8230.627 Road density − 42.635 Agricultural − 111.631 Urban − 684.620 Rail − 122.795 EM zone
13.6 (5–39)
85.193 − 14986.896 Road density − 50.944 Agricultural − 75.631 Urban
20.0 (3–41)
37.462 − 23964.390 Road density + 56.024 Forest − 971.098 Rail − 365.555 EM zone + 249.306 WM zone
18.1 (5–39)
95.779 − 53.111 Agricultural − 75.751 Urban − 175.282 Roads
24.9 (3–41)
42.179 − 21531.726 Road density + 72.848 Forest
31.5 (2–42)
IBI = 61.827 + 76.155 Forest − 289.814 Roads − 771.110 EM zone
23.3 (3–41)
IBI = 62.847 + 82.863 Forest − 441.823 Roads + 908.908 Rail
23.2 (3–41)
Results of tests for the random distribution of four important land cover classes in the Beaverhills watershed
Land cover class
Moran's I index
Z score
p value
Comparison of the proportion of variance in biotic integrity explained (i.e., r2 values) by land cover when land cover classes are regressed on raw IBI scores versus when they are regressed on IBI scores after factoring out any influence of location
Buffer radius (m)
Raw plant-based IBI scores
Plant-based IBI scores with location removed
Raw bird-based IBI scores
Bird-based IBI scores with location removed
Results of GLM predicting plant- and bird-based IBI scores using the proportion of undisturbed land within a series of nested buffers
Buffer (m)
F value
p value
14.010 + 110.440 Undisturbed
16.422 + 86.546 Undisturbed
14.632 + 88.460 Undisturbed
15.703 + 88.194 Undisturbed
15.872 + 89.286 Undisturbed
14.735 + 92.140 Undisturbed
11.218 + 100.400 Undisturbed
13.744 + 104.734 Undisturbed
15.702 + 82.630 Undisturbed
12.497 + 86.960 Undisturbed
10.038 + 92.671 Undisturbed
9.813 + 94.514 Undisturbed
8.594 + 97.563 Undisturbed
5.495 + 105.089 Undisturbed
Land cover predicts biotic integrity
Our results offer support for the use of land cover as an indicator of biotic integrity estimated by both vegetation and bird communities. The variation in land cover within surrounding landscapes was able to explain the majority of variance in IBI scores (70–82 %), but the proportion of variance explained varied with the spatial extent of the landscape considered. We found that bird-based IBI scores were best predicted by land cover within 500 m wide buffers around each wetland, whereas plant-based IBI scores were best predicted by land cover within 100 m wide buffers around each wetland. Mack (2006) and Mita et al. (2007) found similarly high r2 values in predicting vegetation-based IBI scores with measures of land cover surrounding depressional wetlands (72 and 65 %, respectively); however, neither study examined multiple spatial extents.
Based on our results, use of GIS data to complete region-wide assessments without site visits would introduce remarkably little error. The success of our land cover-based models supports previous studies suggesting that bird and vegetation communities are related to land cover variables, even over large spatial extents (e.g., Fairbairn and Dinsmore 2001; Brazner et al. 2007; Luoto et al. 2007). The relationship between biotic integrity and land cover is likely the result of multiple processes. For example, wetlands situated in disturbed landscapes suffer increased exposure to environmental stressors (Crosbie and Chow-Fraser 1999; Houlahan and Findlay 2004), increased nest predation (Phillips et al. 2003), increased invasion by exotic species (Galatowitsch et al. 2000), and reduced habitat connectivity, which has been associated with reduced richness of waterbirds (Guadagnin and Maltchik 2007). It is, therefore, not surprising that land cover surrounding a wetland is predictive of that wetland's biotic integrity as measured from plants and wetland-dependent songbirds and shorebirds.
Several factors could be responsible for the slight differences between model-estimated and observed IBI scores, including simple environmental variability. In addition, there may be a time lag between when a change occurs on the landscape and when the biota responds noticeably to that change. This is especially problematic where the disturbance is expected to increase local extinction rates or decrease local re-colonization rates, as long lived residents will temporarily mask these effects (Findlay and Bourdages 2000). In such cases, land cover data may warn of impending impacts to wetland biota.
Although the variables included in the models predicting IBI scores varied with spatial extent and with the IBI considered (Tables 1, 2), certain variables emerged as consistently important. For example, road density or the relative cover of roads was an important variable in models predicting bird-based IBI scores for all of the spatial extents considered and for four out of seven of the models predicting vegetation-based IBI scores. Looking at wetland bird communities in agriculturally impacted areas of Minnesota, Whited et al. (2000) found that road density was an important predictor, and that road effects on bird communities were most pronounced at the 500 m spatial extent. Looking at wet meadow vegetation communities in Minnesotan wetlands, Galatowitsch et al. (2000) also identified road density as an important correlate of community composition, and found that the relationship was strongest at the smallest spatial extent that they considered (also 500 m). Thus, our results are in agreement with both of these studies in terms of the importance of roads and the spatial extent at which they are most influential on wetland bird communities.
Practically all models that predict wetland condition based on land cover data include some measure of the amount of disturbed land surrounding the wetland (e.g., Mensing et al. 1998; Mita et al. 2007; Wardrop et al. 2007; Sundell-Turner and Rodewald 2008). Six of our seven models predicting plant—IBI scores and three of our seven predicting bird—IBI scores included agricultural and urban land covers. Those models which did not include agriculture and urban covers all included forest cover. The combination of agricultural and urban land covers constitute the majority of land disturbed by human activity, whereas railway lines, cutlines, and roads constitute only small fractions of the landscape on an area basis. In contrast, forested land makes up the bulk of undisturbed land cover at the northern edge of the prairies where the Beaverhills watershed is situated. Because of the collinearity in land covers, the proportion of agricultural and urban land covers might be considered the inverse of forest along a gradient of disturbance. Thus, all the models predicting IBI scores include some measure of the amount of land disturbed by human activities, either directly (agriculture + urban) or indirectly (forest).
The only prominent difference in the subset of land cover classes that predicted vegetation-based IBI scores versus those classes that predicted bird-based IBI scores was that bird IBI models more often included railway lines and emergent vegetation. This suggests that these land cover classes may have a greater effect on birds than on plants. Birds are sensitive to noise disturbance, both because it masks predator arrival and alarm calls and because it interferes with songs related to territory defense and mate attraction (e.g., Slabbekoorn and Ripmeester 2008). Certainly railway lines may act as major sources of noise. Similarly, birds rely on emergent vegetation zones for nesting habitat (Delphey and Dinsmore 1993), and thus the area of emergent vegetation available could have an important effect on bird communities. It is unclear to us why the area of emergent vegetation was not predictive of the biotic integrity of wet meadow plants. Possibly the scale of the imagery did not match the scale at which the vegetation was sampled in the field.
Spatial extent influences relationship between land cover and biotic integrity
The appropriate spatial extent at which to evaluate land cover data is contingent on what taxon forms the basis of assessments of biotic integrity. The importance of spatial extent has been noted before (e.g., Turner et al. 1989; Wu 2004; Houlahan et al. 2006; Brazner et al. 2007), yet previous efforts to develop GIS-based assessment tools typically test only a single spatial extent and rarely provide a biological rationale for its selection. In stream or riverine wetland studies, land cover data is often extracted from within the watershed (e.g., Miller et al. 1997; Falcone et al. 2010) or at the scale of ridge tops and valleys (Wardrop et al. 2007), but in small depressional wetlands with complex surface–groundwater interactions, topography is not necessarily the appropriate basis on which to make decisions about landscape extent (Devito et al. 2005). Previous authors acknowledged the absence of an obvious landscape extent appropriate for depressional wetlands (e.g., Brown and Vivas 2005; Reiss and Brown 2007). Typically, authors either select landscape extents arbitrarily or adopt values published in the literature. Studies that we reviewed examined buffers ranging from 100 m to 3 km wide, which is also the range over which we found models capable of predicting IBI scores using land cover data, but the most frequently adopted extent in the studies we reviewed was 1 km. In our study system, this was larger than the optimal spatial extent for either the vegetation or the bird community.
Although all seven spatial extents that we considered yielded statistically significant models, their predictive strength varied. Changing extents is known to influence certain landscape pattern metrics (Turner et al. 1989; Wu 2004), but we believe that variance in the strength of relationships between biotic integrity and landscape composition across spatial extents reflects the real spatial nature of the relationship between biota and their surroundings. In other words, the predictive strength of our models is greatest when the scale of analysis approximates the operational scale of the taxon in question (Wu 2004). Plant-based IBI scores were best predicted at the smallest spatial extent we considered (within 100 m radius buffers), whereas bird-based IBI scores were most strongly and significantly related to land cover within 500 m radius buffers. We attribute this discrepancy to the fact that unlike stationary, passively dispersing plants, birds are mobile and actively select their habitat. In other words, they have a larger functional grain size (sensu Romero et al. 2009). Most of the passerines important to the bird-based IBI have breeding territories less than 1 ha, but many area-sensitive species like Wilson's Pharalope (Phalaropus tricolor), Black Tern (Childonias niger), Marbled Godwit (Limosa fedoa), and American Avocet (Botaurus lentiginosus), appear to be using 100 ha areas (Dechant et al. 2001; Dechant et al. 2002; Zimmerman et al. 2002; Dechant et al. 2003), which because of the small size of our wetlands, corresponds with a landscape with a buffer radius of about 500 m (the area encompassed by the 500 m buffers ranged from 91.4 to 147.1 ha, with a mean of 112.6 ha). The idea that the optimal landscape extent for assessing biotic integrity might be species-specific is supported by other bird studies. For example, Tozer et al. (2010) found that both Marsh Wren and Least Bittern abundances were related to the proportion of wetland in the surrounding landscape, but at different spatial extents. They suggest that the positive relationship between wetland habitat and bird abundance is due to a greater influx of dispersing individuals in landscapes with a greater proportion of wetland habitat, although such a mechanism would apply equally to plants through the dispersal of their propagules, either by wind, water, or animal vectors. The area of wetland cover within our buffers did not emerge as a generally important predictor of IBI scores, although measures of natural habitat (e.g., forest cover) or its complement (e.g., agricultural and urban land cover) were consistently important in our models and could also influence dispersal. Regardless, the critical influence of spatial extent on the strength and significance of models relating biota to the surrounding landscape is clear. Our findings warn against the arbitrary selection of landscape extents, especially in depressional wetlands where appropriate extents are not dictated by topography.
Spatial autocorrelation
We were surprised that land cover was able to explain more than half the variance in IBI scores, even 3 km away; especially for non-mobile plants when soil storage capacities in the region are so high that run-off is minimal (Devito et al. 2005) and the watershed transportation capacity is very low (i.e., the wetlands are fairly isolated). One explanation we considered was that land covers were positively spatially autocorrelated such that, for example, the abundance of forest 3 km away is able to predict the biotic integrity of a wetland because it is predictive of the amount of forest adjacent to the wetland, not because it is directly affecting communities within the wetland across such a large distance. In other words, we were concerned the observed correlation between land cover at large spatial extents and IBI scores could be spurious. Positive spatial autocorrelation is common in nature (Legendre 1993), and indeed the four land cover classes most commonly included in our models exhibited significantly clumped distributions (Table 3).
To confirm that our conclusions about the capacity of land cover to predict IBI scores and the optimal spatial scales at which land cover should be considered were not merely the result of positive spatial autocorrelation, we factored out the influence of location on IBI scores and then re-ran the models. This process revealed a significant East–West gradient in IBI scores for both the plant- and bird-based IBIs: most likely the result of the presence of the City of Edmonton in the West of our study region and the national and provincial parks in the East (Fig. 1). Earlier work in the Beaverhills watershed (e.g., Rooney and Bayley 2012; Wilson and Bayley 2012) determined that urban wetlands are typically more disturbed than those in agricultural or protected areas, so this gradient in biotic integrity was not unexpected. Yet, when we re-ran the GLM after factoring out the influence of location, our conclusions were unchanged. Significant models predicting IBI scores using land cover data were generated for all seven spatial extents, and the optimal spatial extent for predicting the plant-based IBI remained smaller than that for predicting the bird-based IBI (Supplementary Table S2).
Regressing land cover on IBI scores after factoring out any influence of location is a conservative approach to confirming our results, as it excludes any correlation with biotic integrity shared by land cover and location, regardless of the underlying mechanism (Fig. 3). Our goal in this study was not to tease apart mechanisms driving the relationship between estimates of biotic integrity and land cover. Rather, we sought to answer two questions fundamental to the development of GIS-based wetland assessments: (1) whether a predictive relationship between land cover and biotic integrity in wetlands of the Beaverhills watershed exists; and (2) at what spatial extent such relationships are best estimated. The fact that our main conclusions were unchanged regardless of the exclusion or inclusion of location's influence on IBI scores suggests strongly that remotely sensed land cover could serve as the basis for reasonably accurate region-wide assessments of wetland condition.
"Undisturbed" models yield poorer predictions of biotic integrity
In rapidly developing regions or when year-to-year variability in budgets and conservation opportunities impede the immediate implementation of province-wide reserve networks, simple models for identifying conservation priorities out-perform more detailed models (Meir et al. 2004). Thus, single variable measures of wetland condition, like percent undisturbed land within 100 m, are of obvious appeal. This is especially true where management units encompass a patchwork of land cover maps, each created with different data sources, at different resolutions, constructed using different land cover classification approaches. In such cases, reducing all maps to a common denominator (a binary system of disturbed and undisturbed cover types) may overcome many of the challenges presented by integrating such variable components. Previous work has revealed evidence that single-variable models can be predictive of habitat quality. For example, looking at prairie pothole wetlands in North Dakota, Mita et al. (2007) found that the percent cover of grasslands explained 72 % of the variance in a vegetation-based IBI score.
Unfortunately, our results suggest that the amount of undisturbed land cover is a relatively poor surrogate for more detailed models of biotic integrity in depressional wetlands. At all spatial extents, the "undisturbed" model explained substantially less variance in IBI score than the models based on more detailed classifications of land cover data. Allowing for optimization of landscape extent, the best we could achieve using the "undisturbed" model was to explain 63 % of the variance in plant-based IBI scores and 57 % of the variance in bird-based IBI scores. In contrast, our best models using detailed land cover data explained 82 and 70 % of the variation in IBI scores for plants and birds, respectively. If we constrain the spatial extent to the 100 m buffer width (as was recommended by policy makers within our study region), there is an even greater loss of predictive capacity: only 51 % of variance in plant-based IBI scores and 38 % of variance in bird-based IBI scores is explained by the simple models at the 100 m extent. This surprised us, especially for the plant-based IBI, as the backwards stepwise modeling with more detailed land cover data identified the 100 m buffer width as the optimal landscape extent. Thus, it is important to consider the appropriate extent of the landscape, regardless of whether land cover is considered holistically or is reduced to a single representative measure like the proportion of undisturbed habitat.
In terms of the balance between model accuracy and complexity, the AICc values associated with the "undisturbed" models were much larger than those associated with the models using detailed land cover data, despite having one-fifth the number of predictor variables (Tables 1, 2, 5). Thus, we observe that although single predictor variable models can predict biotic integrity (i.e., the simple models were statistically significant, with p < 0.05), incorporating more detailed land cover data substantially improves both the strength and accuracy of IBI score predictions. Furthermore, measures like the proportion of undisturbed land cover are typically derived from existing GIS datasets that contain more detailed information about land cover. In such instances, the accuracy of wetland assessments could be substantially improved by using the detailed land cover data to estimate IBI scores rather than using it to calculate simple proxies like the proportion of undisturbed land cover.
The ability to use remotely sensed data in place of intensive assessments that require site visits should inform land use planning and the identification of areas of high conservation or restoration potential (Sundell-Turner and Rodewald 2008). Without understanding the mechanisms by which land cover and biotic integrity of wetlands are connected, GIS-based assessments will not be diagnostic of the cause of biological impairment (King et al. 2005). Rather, in areas where GIS-based assessment suggests impairment, more intensive field-level assessment will be required to confirm and to identify the cause of impairment (Brooks et al. 2004). Thus, we envision a system where GIS-based and field-level assessments are used in concert to facilitate a flexible, adaptive approach to wetland management.
Despite extensive evidence that different taxa interact with their surroundings at different spatial extents, most studies relating the abundance or diversity of biota to land cover explore only a single spatial extent. Especially in the case of depressional wetlands, justification for using a given spatial extent is usually poorly articulated. Due to the important influence of spatial extent on model performance, we recommend that the optimal landscape extent be determined through a calibration process such as the one we undertook, wherein biotic integrity is modeled using land cover data extracted from a variety of spatial extents within a test area before that model is applied to a larger region. Such calibration efforts will help ensure that wetland assessments made using remotely sensed data provide reliable estimates of actual wetland conditions.
Our models have been calibrated within the Beaverhills watershed, so we are able to successfully predict biotic integrity using land cover data within this region. The next logical question becomes how far beyond the calibration region will our models hold before we risk over-stepping their predictive capacity? One important constraint in evaluating this question is that IBIs are also regionally constrained in their application (Karr 1993; Mack 2007). The IBI scores we sought to predict are validated for the Aspen Parkland Ecoregion (the transition zone between the northern prairie and southern boreal habitats), so it stands to reason that our landscape models should be evaluated using the same biotic integrity indices measured across the Aspen Parkland in order to identify the limits to extrapolation.
We thank Andrew Forrest, Matt Bolding, Adam Spargo, and David Aldred for assistance with data provision and the Alberta Water Research Institute, Alberta Innovates Technology Futures, and the Beaverhills Initiative for funding and in kind support. We also thank Dr. Jianguo Wu for suggesting references and two anonymous reviewers for their insights.
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Supplementary material
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How to send email to an address entered on a formAsked by christiaan pretorius on May 20, 2016 at 12:52 PM
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"Super sexy opera star. opera singer (Baritone), when Stephen gets through with him he will be a...Nathan Gunn
"Nation, don't be a stranger! Unless you see him on the street, then, by all means: be a...The Colbert Report/Episodes/EpGuide/Episode 392
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Washington, Dec. 12: The Bush administrations Iraq policy has fallen from the proverbial frying pan into the fire.
Even as the White House tried to defuse a growing tide of criticism over its decision to ban countries like India from contracts in Iraq, the effort was neutralised yesterday by an auditors finding that the US defence conglomerate, Halliburton, had overcharged the Pentagon by at least $61 million for fuel supplied to Iraq since the war.
Halliburton was headed by vice-president Dick Cheney until he became George W. Bushs running mate in the November 2000 election and it was awarded Iraqi contracts worth several billion dollars without competitive bidding soon after the war.
Pentagon auditors revealed that in a second case, a Halliburton subsidiary, Kellogg, Brown and Root, submitted a proposal for cafeteria services in Iraq that was inflated by a whopping $67 million, but the mismatch was discovered and the proposal was rejected. The revelations have come at the worst possible time for President Bush: there is allround surprise here that even Republican leaders are coming out openly against the Pentagons latest policy of barring countries which did not support the war from undertaking rebuilding work in Iraq.
Yesterday, Bill Frist, the Republican leader in the Senate, who was hand-picked by Bush for the job, publicly disagreed with the policy.
Appearing on CNBCs Capital Report, Frist said: We have to remember that many of these countries that are being denied these contracts are supporting us elsewhere in the world, maybe fighting HIV-AIDS in Africa, maybe in Afghanistan, and that is why I hope that there will be some moderation of the policy as we go forward.
Speaking for the Democrats, Congressman Henry Waxman said the audit report confirms what we have known for months: Halliburton has been gouging taxpayers and the White House has been letting them get away with it.
He said in a statement that it is deplorable and we need to put an immediate end to it. There needs to be a top-to-bottom review of all the Iraqi contracts.
Auditors found that Kellogg, Brown and Root was overcharging the US government $1.09 per gallon of fuel delivered to Iraq. About 57 million gallons of fuel was delivered at inflated prices.
Yesterday, Senator Frank Lautenberg, a Democrat, demanded Senate hearings on the auditors findings. Responding to the findings, Dov Zakheim, the Pentagons budget chief, assured that contractor improprieties or contract mischarging on department contracts will neither be condoned nor allowed to continue.
Halliburton, meanwhile, went on the offensive arguing that the risks involved in transporting oil to Iraq made it necessary to charge high prices.
Wendy Hall, a spokeswoman for the company, was quoted by news agencies as saying that more than 20 of the Halliburtons trucks had been damaged or stolen and that nine of its drivers had been injured and one killed while executing the fuel supply contracts.


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Hagerty Editorial and Digital Media Manager Claire Walters has earned the nickname "ClaireDevil" around the office, due to her willingness to volunteer for some of our more daring story ideas. For her "Breit Skies" story in the summer issue of Hagerty Classic Cars magazine, Claire went up in a Breitling stunt plane. You'll have to watch the video to see whether her lunch stayed down.
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Where: Waynedale Park, Intersection of 2900 Koons and Elzey Street- just off of Lower Huntington Road
What: The picnic is a family friendly event that has been a long-standing tradition in the Waynedale community, drawing over 2,000 attendees yearly. Made possible by over a hundred local businesses and organizations who have donated to the community event. There will be free lunch and snacks. Fun to be had with bingo, kids games, inflatable obstacle course, appearances from our local police, EMT and fire departments, Science Exploration Tent, face painting, vendors and more.
Cost: Event is FREE to residents in the Waynedale area
There are a number of various sponsorship levels in which you or your company could lend a hand for the annual picnic. No matter what the level of sponsorship, your support is vital to the success of this event year in and year out. We rely heavily on the support of local people and businesses from the community. There are many perks designed for our sponsors, but most importantly this is an impactful way to be part of a great community event and show your support for Waynedale!
Click here for sponsorship information
by Megan Ryan, article from The Waynedale News
And just like that, the 13th Annual Waynedale Community Picnic, has come and gone. My first year as event coordinator was a whirlwind of fun and excitement! It was an amazing experience to watch so many people come together in one place and enjoy each other's company. The Waynedale Community is a vibrant place full of residents who truly care about the place where they live. This was proven to me this past Saturday as I watched the picnic unfold. From seeing kids enjoying the splashpad, to watching families and neighbors enjoying their lunches together it was a day filled with togetherness and community building!
Putting together an event of this size is no small task, but it was apparent that the work paid off as I reflected on what the Waynedale Community Picnic brought to the people of this area. The smiles and laughter that I was privileged to witness for an entire afternoon were testimony to the purpose behind why we host the picnic each year in Waynedale. We are proud to announce that this year we had a record number of attendees; topping out at around 2,000 people. Wow! This simply means that we had the opportunity to reach out to even more people from the community this year and help provide them with a day of enjoyment and entertainment.
I have a huge amount of thanks to give to the two people who were the legs behind such a wonderful event: Alex Cornwell and Cheryl Connett. Our small, but mighty, team of organizers worked tirelessly throughout the spring and summer to ensure that the Waynedale Community Picnic was a day to remember for those who attended. Of course, an event like this for the community also means that many, many volunteers are needed to help make the day run smoothly. From 7am-7pm the volunteers gave of their time and energy and we cannot thank them enough for the hard work they put in and the efforts they went to for the sake of the picnic.
It would behoove me to also take this time to thank our ever-so-generous sponsors and vendors who helped make the picnic a reality. All the work in the world can go into planning an event such as the Waynedale Community Picnic, but without the support of our local businesses and organizations we simply could not accomplish what we did with the picnic this year.
Just over 115 Waynedale businesses came together to support and sponsor the picnic this year. Our sponsors impacted every facet of the day. From pizza sponsored by Pizza Hut, to the music and sound equipment donated and operated by Circle City Audio, to the generous support of Hanson Aggregates, GraphX and JW Signs, to the intricate planning and promotional support of The Waynedale News, I am incredibly thankful! On top of our sponsors, more than 40 area businesses came out to be vendors and spend the day at the park visiting with the community and sharing in the excitement of the day.
Needless to say, this past spring, when I was approached about taking over the coordination and planning of the picnic, I accepted without realizing what a wonderful opportunity I was truly being given. I am honored that I was asked to take on such an important task for the Waynedale community and it is from the planning of the event and the picnic itself that I have gotten a huge amount of joy. Thank you, Waynedale!


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get the drift
get the driftalso catch the drift
to understand in a general way what someone is telling you I usually read the first page of a report just to get the drift.
Usage notes: sometimes used in the form get someone's drift: She said something about going home, but Len didn't get her drift at the time.
get the drift
Also, catch the drift. Understand the general meaning or purport. For example, I didn't get the drift-do they want to go or not? or Over all the noise he barely managed to catch the drift of their conversation. The noun drift has been used for "purport" since the early 1500s.


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Nodes of term Heath Bunting
Heath Bunting was born a Buddhist in Wood Green, London, UK and is able to make himself laugh. He is a co-founder of both net.art and sport-art movements and is banned for life from entering the USA for his anti GM work. His self taught and authentically independent work is direct and uncomplicated and has never been awarded a prize or been bought or sold. He is both Britain's most important practising artist and The World's most famous computer artist. He aspires to be a skillful member of the public and is producing an expert system for identity mutation.


text: Home > Global Filipino PH's best showcased in Ottawa by Anne Wong, Balitang Canada Posted at Aug 03 2012 10:18 AM | Updated as of Aug 03 2012 06:18 PM Share Save Facebook Twitter GPlus LinkedIn CANADA – Distinguished local and foreign guests were treated to a showcase of the Philippines' best at the Ambassador's residence Tahanang Rizal in Ottawa. "They told us that they like Filipinos, of the closeness of our cultural values. So we will show to them that Filipinos and Canadians are very much compatible," said Philippine Ambassador to Canada Leslie Gatan. Non-Filipino guests also adored the paintings and talked about owning a barong tagalog and eating halu-halo in the summer time. Ottawa Mayor Jim Watson, meantime, raved about Philippine delicacies and enjoyed meeting the Toronto performers and Filipinos visiting the ambassador's residence. "We are blessed in our city to have a very vibrant, dynamic Filipino-Canadian community and certainly many members of the Filipino community do come and immigrate to Ottawa to Ontario and that helps our economy," the mayor said. Share Save Facebook Share on Twitter GPlus LinkedIn Read More: Canada /life/08/20/17/alamin-paano-maiiwasan-ang-premature-skin-aging/video/entertainment/08/20/17/empoy-binalikan-ang-dating-buhay/sports/08/20/17/sea-games-gilas-escapes-with-difficult-win-over-thailand/sports/08/20/17/sea-games-silver-shines-like-gold-for-young-archer-tagle/video/life/08/20/17/dating-principal-janitress-na-ngayon


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Sr. Systems Engineer - Sentinel (SIEM) - Washington, D.C. 8/10/2016
Micro Focus
Washington, DC
JOB DESCRIPTIONAPPLY The Dedicated Support Engineer (DSE) is assigned to support one (1) of Micro Focus' largest and most strategic customers with post-implementation product support. The DSE will be on-site four (4) days per week.
The DSE will develop business relationships with the customer's technical and management staff, and support the customer by gaining first-hand knowledge of their system, configuration and business needs. The engineer will use this familiarity to accelerate response time to requests and ensure that solutions are tailored to meet customer expectations. The engineer's goals are to pro-actively help the customer avoid potential problems and maintain the environment of Micro Focus technology in an optimal and efficient manner. The engineer will be available to respond in a timely manner (defined in the contract) to any issues that may arise with the implemented Micro Focus technologies and will utilize resources within Micro Focus as needed to assist with these issues. An engineer will also listen to the needs of their customer for opportunity discovery and forward to the appropriate internal team (Sales, Services) for further development.
In this position, the DSE will cover multiple Micro Focus products and need to be a quick study on new product functionality and features. The products requiring strong experience are Sentinel and Privileged Access Manager. The DSE will need a broad mix of skills and, most importantly, the ability to work well with a structured yet dynamic environment. In addition, the DSE candidate should possess the ability to work well in highly charged situations, have strong teaming skills and the ability to provide outstanding customer service.
Performance Expectations and Key Responsibilities
* Form strong business relationships with the customer at various levels throughout the organization.
* Be available 24x7 for the customer. Primarily 5x12 for all issues and 24x7 for critical issues.
* Troubleshoot and provide accurate and timely resolution to customer issues.
* Serve as a customer advocate within Micro Focus by facilitating escalation of issues through appropriate internal organizations.
* Pro-actively identify issues by becoming familiar with customer's technical and business environment while providing recommendations for resolution to these issues.
* Prevent customer issues by reviewing their Micro Focus environment regularly, report and correct issues per customer requirements.
* Share knowledge by writing technical documents and pro-actively sending helpful technical information to customer (upcoming patches, technical news, etc.).
* Document and communicate work performed in provided systems and documents as requested.
* Deliver technical presentations and/or training to diverse audiences.
* Assist in the implementation of Micro Focus products/solutions in line with specific Consulting engagements.
* Participation in projects and project management as requested by customer or Micro Focus.
Required Education/Experience
* Bachelor's Degree in Computer Science, Information Systems or related field or equivalent experience required.
* 5+ years IT/Micro Focus Services experience with product expertise with Sentinel and Privileged Access Manager.
* Position requires a strong sense of responsibility, self-motivation and the ability to prioritize and organize workload to complete multiple, simultaneous projects.
* Position requires flexibility and capacity to work across a variety of activities within a matrix and virtual organizational framework.
* Ability to appropriately handle tense/stressful customer situations regarding service outages and issue resolution.
* Able to influence both internal and external stakeholders
* The ability to work seamlessly across organizations.
* DOD Secret clearance, or willingness to complete clearance process
* Willing to travel as needed
* May at times be required to work outside of standard business hours
* Location: Washington D.C.
The Attachmate Group Companies is an equal employment opportunity employer. We comply with all applicable federal, state and local laws in providing our employees and applicants with equal opportunity. Our decisions and criteria governing the employment relationship with all employees are non-discriminatory. They are made without regard to race, color, religion, creed, gender, veteran status, sexual orientation, age, national origin, and citizenship, the presence of a physical, mental or sensory disability, marital status or any other status protected by law.
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Product demonstrations - at your office or at a local "Demo Site" or "Showrooms" of specific products and applications.
You've heard the phrase before—may we be of service? No one else takes it to the extent that Rite-Hite and our exclusive Representative Network do.
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text: 3. As of noon trading, Sony Corporation ( SNE) is down $0.15 (-0.9%) to $16.50 on light volume Thus far, 833,711 shares of Sony Corporation exchanged hands as compared to its average daily volume of 3.8 million shares. The stock has ranged in price between $16.30-$16.50 after having opened the day at $16.44 as compared to the previous trading day's close of $16.65. Sony Corporation designs, develops, manufactures, and sells electronic equipment, instruments, and devices for consumer, professional, and industrial markets worldwide. Sony Corporation has a market cap of $16.4 billion and is part of the consumer durables industry. The company has a P/E ratio of 4.6, below the S&P 500 P/E ratio of 17.7. Shares are up 48.7% year to date as of the close of trading on Friday. TheStreet Ratings rates Sony Corporation as a hold. The company's strengths can be seen in multiple areas, such as its impressive record of earnings per share growth, largely solid financial position with reasonable debt levels by most measures and good cash flow from operations. However, as a counter to these strengths, we also find weaknesses including poor profit margins and a generally disappointing performance in the stock itself. Get the full Sony Corporation Ratings Report now. Exclusive Offer: Jim Cramer's 'go-to' small/mid-cap guru Bryan Ashenberg only buys stocks he thinks could return 50-100%. See his top picks for 14-days FREE.


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Wildcats Drop Sunflower Showdown
Wildcats Drop Sunflower Showdown
LAWRENCE, Kan. (AP)Lamark Brown rushed for three scores but Jake Sharp had 257 total yards and tied a school record with four touchdowns, as Kansas defeated rival Kansas State 52-21 on Saturday.
Kansas (6-3, 3-2 Big 12) was reeling after giving up more than 1,200 yards in losses to Oklahoma and Texas Tech the previous two weeks. The Jayhawks bounced back quickly, defeating Kansas State (4-5, 1-4) in the 106th meeting between the instate rivals.
Kansas ran for six touchdownsone short of the school recordand harassed Kansas State quarterback Josh Freeman into four turnovers, giving him 14 during Kansas' first three-game winning streak in the Sunflower Showdown since 1988-90.
Brown was the only bright spot for Kansas State, scoring on runs of 4, 11 and 1 yards, though all three came after the game was out of reach.
Sharp had 118 yards on nine carries and scored on runs of 4, 20 and 47 yards in the first quarter to put Kansas up 21-0. Sharp added a 2-yard touchdown run in the third to put Kansas up 45-7 and finished with a career-best 181 yards rushing. He also caught five passes for 76 yards.
On the other side of the ball, Kansas gave Freeman fitsagain.
The junior had five interceptions and four fumbles his first two games against Kansas, and had given the ball away three more times before the first half was over in this one.
Freeman threw an interception on his third pass attempt of the game, the ball fluttering into the hands of Kansas' Russel Brorsen at the Jayhawks' 42 after he was hit from behind by defensive end Jake Laptad. Sharp scored 70 seconds later, bursting off right tackle on a 20-yard run.
Freeman finally got the Wildcats going late in the second quarter, converting a pair of fourth downs to get the Wildcats to the 1-yard line, only to lose the ball while trying to reach over the goal line. Kansas recovered and methodically marched 80 yards for a 15-yard touchdown run by quarterback Todd Reesing to go up 31-0.
Freeman's third turnover of the first half came on a Hail Mary pass in the end zone after Kansas State knocked down Jacob Branstetter's 33-yard field goal try, it's nation-best eighth blocked kick of the season. Freeman also fumbled in the third quarterKansas State recoveredand threw his third interception in the fourth.


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In a recent tweet by Battlefield's official account
Straight from the video editing suite: Prepare 4 Battle - 03.27! This sneak peek now, more soon.
A video has been uploaded that shows pretty much gibberish and with 'Prepare 4 Battle' and a date of March 27th which indicates that the BF4 Reveal is coming soon.
Excerpt from Battlefieldo
hich shows close-up shots of what appear to be mechanical parts of an engine or vehicle, presumably an armored vehicle considering the continuous tracks we see later on. The montage cuts to a big, white 4 with an orange border. A slogan appears on the screen, "Prepare 4 Battle" and then a date appears in the video - 03.27.
Source: Battlefieldo
My news posts in a year, my writing 'skills' is rusty.
Goodbye, see you guys in October


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In any engineering entrance examination Chemistry is one of the important subjects. In this article, students preparing for IIT JEE will learn some important tips and strategies associated with Chemistry section of the exam. They will also understand the common errors while attempting the paper.
For many, Chemistry is the easiest part of the JEE paper. In JEE, you get 30 questions in the Chemistry section which is same as Physics and Mathematics. The level of difficulty of the entire set is not different than the other two subjects. Most students can finish this section in shortest time and hence attempt this section first.
Some concepts need to be learnt and many can be conceptually understood.
The Main Mantra
On a Chemistry paper, typically about one third questions are numericals and others are conceptual questions. A Chemistry paper has questions from all three areas: Physical Chemistry (mainly numericals), Inorganic Chemistry and Organic Chemistry.
In Chemistry, more focus should be given on understanding the concepts given in a chapter. Chemistry is not about cramming and many questions can be logically solved. You should make notes on all chapters noting down important tips and tricks.
Solve first few questions, about 30, on each chapter with notes and books open. Then, once you are comfortable with questions, you can do them without reference. With every question you do and understanding how it is solved, you may update your notes.
How to approach Chemistry Syllabus
For Physical Chemistry section, where mostly numericals are asked, you must make a note of all formulae required. For most questions, calculation are very simple and many important values are already given in the question itself. So make a note of what is asked and then identify the formula to apply. Find out the required inputs to the given formula in the question or derive them if they are not directly given.
Inorganic Chemistry and Organic Chemistry section may seem a little difficult to many as it requires remembering a lot of facts. You can use mnemonics to remember things, for example, many mnemonics are available for remembering the Periodic Table. You can also use flash cards to learn all these concepts. There are some rules applying to compound formation or compound naming which can be easily learnt and applied to some questions.
For Organic Chemistry, as rule master the chapter "General Organic Chemistry" and other advanced concepts in Organic Chemistry will become easier to learn.
Some JEE Tricks
Since many questions of Chemistry can be solved mentally and without the need of a pen and paper, you must practice a lot of questions in every study session. This will help you strengthen your concepts and will make you familiar with a variety of questions asked in the exam.
Remember to do previous year questions from each chapter.
You can pick the Chemistry section to solve the first in the JEE exam. In this section as well as on the entire paper, if you find the question difficult to solve move on to the next question and come back to it later. If you continue to struggle with it you may lose time for other questions.
It is easy to get overconfident on the Chemistry section and make mistakes but be patient and have the presence of mind while solving the questions.
Author Name: Yogita Bhalla
Description: Yogita Bhalla is Founder and CEO of Simplylearnt.com and has a passion for helping students excel in their lives. She has appeared for 6 different competitive exams (including NTSE, IIT-JEE, GRE) and has gone through the toiling preparation process. She believes that study material can be made more engaging for the students and hence they can fall in love with test preparation.


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The Kahnawake-licensed operator stated that AllBingoClub.com uses the latest technology to bring 90-ball bingo to players, who can take advantage of a special ten-dollar introductory offer alongside a full first-time deposit bonus up to $200.
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- Babel
- Babel Ba"bel, n. [Heb. B[=a]bel, the name of the capital of
Babylonia; in Genesis associated with the idea of
1. The city and tower in the land of Shinar, where the
confusion of languages took place.
[1913 Webster]
Therefore is the name of it called Babel. --Gen. xi. 9. [1913 Webster]
2. Hence: A place or scene of noise and confusion; a confused mixture of sounds, as of voices or languages. [1913 Webster]
That babel of strange heathen languages. --Hammond. [1913 Webster]
The grinding babel of the street. --R. L. Stevenson. [1913 Webster]
The Collaborative International Dictionary of English. 2000.


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Register To Attempt Complete Test
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Practise Test 1
Eight persons, Amit, Bharani, Chetan, Dhruv, Sohan, Tariq, Umran and Vinay, sit in two parallel rows with four persons sitting in each row facing one another. Amit and Bharani are not in the same row. Sohan sits to the immediate left of Chetan in the same row but opposite to Tariq. Umran and Bharani have only two persons sitting between them. Amit and Sohan have only one person between them.
Which of the following pairs sit diagonally opposite to each other?


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Chocolate Guinness Cupcakes
Recipe by SweetVeggie
This is a twist on traditional chocolate cupcakes for St. Patty's day. I combined a few recipes I found on the web, just storing it here for safe keeping!
Top Review by Marg CaymanDesigns
I made these for my dad for St. Patrick's Day since he loves chocolate cake as well as Guinness beer. He loved them! They are a rich and moist. The directions on the recipe are for cake not cupcakes. I baked mine for 18 minutes. The recipe makes 24 not 12. Obviously the person who posted the recipe hasn't tried it yet. We thought they tasted much better the next day when the beer flavor wasn't as strong and the chocolate was more predominate. I iced them with my favorite butter cream frosting, Recipe #261024.
Ingredients Nutrition
- 147.89 ml unsalted butter
- 236.59 ml Guinness stout
- 177.44 ml unsweetened cocoa
- 473.18 ml sugar
- 177.44 ml reduced-fat sour cream
- 2 large eggs
- 473.18 ml all-purpose flour
- 14.79 ml vanilla extract
- 12.32 ml baking soda
Directions See How It's Made
- Pre-heat oven to 350 degrees.
- Grease a 9-inch springform pan and line with parchment paper.
- In a large saucepan, combine Guinness and butter.
- Place over medium-low heat until butter melts, then remove from heat. Add cocoa and sugar, and whisk to blend.
- In a small bowl, combine sour cream, eggs and vanilla; mix well.
- Add to Guinness mixture. Add flour and baking soda, and whisk again until smooth.
- Pour into buttered pan, and bake until risen and firm, 45 minutes to one hour. Place pan on a wire rack and cool completely in pan.


text: Simplicity 4386; ©1953; Girl's One-Piece Jumper, Dress, Blouse and Detachable Collar: This "Simple To Make" jumper and dress has a fitted bodice and back button closing. Flared skirt has patch pockets. View 1 blouse has three-quarter length kimono sleeves. Blouse is styled with back buton closing and roll collar. Top stitching trims jumper, Views 1 and 2. View 2 features bow at back neck edge. View 3, bodice and skirt of contrast fabric, is worn with detachable collar.


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Wax Furniture Polish
SKU: 100712| Size: 400 ml
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Take care of your furniture in one easy step with this special blend of wax and silicone. It cleans, polishes and protects all wooden surfaces.
As well as containing an anti-static agent, this rich creamy non-drip formula also provides a splendid satin finish and leaves a fresh, clean lemon scent.
- Contains a special blend of wax and silicone
- Contains an anti-static agent
- Non-drip formula
- Cleans, polishes and protects all wooden surfaces
- Leaves a fresh, clean lemon scent
Contains more than 5% but less than 15% Aliphatic Hydracarbons. Contains less than 5% Nonlonic Surfactants. Also contains Perfume, Citral, Limonene
Shake well before use. Hold can 20cm away from surface and spray as required.
Avoid contact with eyes. Use only in well-ventilated areas.
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… This week I was reading a post from a digital marketing agency on why you should hire them. The first and foremost reason was digital marketing expertise. I'm not sure I agree with that at all – the majority of companies we work with that have a marketing department in place have incredible expertise and we often learn from them just as they're…


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The moment you…..
- Realise you should've eaten lunch after the rides as you whizz around on the Waltzers on Brighton Pier
- Take a backstage tour and discover which legendary actress made use of the Last Gulp theatre bar between scenes when she performed at the Theatre Royal Brighton
- Find a one-off, last-in-stock gem in an independent fashion boutique hidden amongst the twisting alleyways of The Lanes
- And your friends invent a new game called "Who I Fancy The Most" whilst people-watching from behind skinny lattes in the coolest café in the North Laine (there are many)
- Remember you didn't bring a towel as you dive into the sea on Brighton Beach. Oh well, at least you're swimming
Brighton is only one hour away from London Bridge station by train.
The moment you…..
- Spot your first Banksy on a Bristol street art tour with Where The Wall
- Take your first bite of The Scrumpea, a West Country Cheddar pie with summer vegetables and Somerset cider, as served by Pieminister in St. Nick's Market
- Succeed in mastering stand-up paddle boarding on Bristol Harbour with SUP Bristol
- Gain access to Milk Thistle speakeasy bar for an evening of antiquated class and esteemed cocktails
- Ascend the rigging aboard Brunel's SS Great Britain and edge onto the main yard, more than 30 metres above the ground
- Take your first peek over the basket in a hot air balloon ride with Baileys Balloons
Take a train from London Paddington to Bristol in approximately one hour and 40 minutes.
The moment you…..
- Bite into the first tasty morsel of the night at Digbeth Dining Club, the Best Street Food Event in the UK (2014)
- Test your skills as a TV news presenter during a BBC studio tour
- Sample a famous Birmingham balti on a behind-the-scenes tour of the Balti Triangle with Alternative Tours
- Order a glass of Parisian absinthe – poured from a traditional fountain – whilst reclined on a two-person sofa at The Electric Cinema
- Take a Peaky Tour and chart the criminal lives of the real 'Peaky Blinders', the shadowy characters who inspired the BBC TV drama
A train from London Euston to Birmingham New Street takes roughly one hour and 25 minutes.
The moment you…..
- Are served dinner by a prisoner, at The Clink Restaurant
- Take a leap of faith on the trapeze during a beginner's aerial course with NoFit State Circus
- Remember you left your sonic screwdriver at home when the TARDIS needs fixing at the Dr Who Experience
- Discover the next BIG Welsh band playing live at Clwb Ifor Bach – aka The Welsh Club – on the same stage once graced by Super Furry Animals and Catatonia
- Are convinced you see the infamous mournful white lady standing on the river bank during the Llandaff Ghost Walk
- Find a piece of vinyl treasure whilst browsing through the oldest record shop in the world, Spillers Records
Travel to Cardiff from London Paddington in approximately two hours by train.
The moment you…..
- Show off your boarding skills at the new Glow in the Dark Skatepark
- Spot 100 cast iron statues on Crosby Beach – aka Anthony Gormley's Another Place – while on board the Dazzle Ferry sunset river cruise along the Mersey
- Tweet a selfie - #tweetfromthetower - from the top of Liverpool Cathedral during a Twilight Tower Tour
- See into the past as a Beatles tribute band plays live at The Cavern, the same music venue made famous by the original Fab Four
- Visit the most lavish toilets in the UK (men only, sorry ladies) at the Philharmonic Dining Rooms, which is also home to an array of fine craft ales
- Squeeze your partner's hand tighter after your first fright on the theatrical Shiverpool Ghost Bus Tour
London Euston to Liverpool is roughly two hours and 15 minutes by train.
The moment you…..
- Take the first sip of your pint as a steam engine blows its whistle on the platform, your departure point for the East Lancashire Railway Rail Ale Tour
- Relive musical history on a Manchester Music Walking Tour
- Understand exactly what it takes to become the UK's Museum of the Year 2015 when admiring the artwork at The Whitworth
- Pick up a chic vintage outfit for less than you were expecting while out shopping in the fashion boutiques of The Northern Quarter (reward yourself with glass of something chilled at the trendy neighbourhood bars)
- Recline in your seat at The Lowry, ready to be entertained, following a day of exploring The Quays
Manchester is just over two hours away from London Euston by train.
The moment you…..
- Get the distance right for that photograph of the Angel of the North standing in the palm of hand
- Show off your most eyebrow-raising dance moves as a live band performs at The Boiler Shop Steamer. Meanwhile, your friends busily chow down at the venue's many street-food stalls
- Suggest stopping off for a local ale at a riverside pub so that you discuss the contemporary art you've just witnessed on a guided tour of BALTIC on the Quayside
- Step inside the mirrored cloud-like structure of The Sage Gateshead, filled with curiosity before a live performance from the Royal Northern Sinfonia
- Breathlessly reach the top of Grey's Monument in Grainger Town and marvel at the classical architecture below, as designed by Richard Grainger in the 1830s
Newcastle can be reached by train from London King's Cross in roughly three hours.
The moment you…..
- Pat yourself on the back for cleverly spotting your first Charles Rennie Mackintosh architectural design on the Glasgow Style Walking Tour
- Spin two hoops around your body at once on the Aerial Edge hula hoop class at the iconic 1873 Briggait building
- Put your skills to the test by moving up from the Blue Circuit to the tougher Red Circuit on the Pollok Park mountain bike courses
- Discover the basement club during a Walking Heads Music Tour around GTown, where Franz Ferdinand's Alex Kapranos once hosted 99p nights
- Find a floating taxi, created by street artist Rogue One, during the Glasgow City Centre Mural trail
Glasgow is just under five hours from London Euston by train.
The moment you…..
- Learn what life was like during the 19th century on a guided Mercat Tour of the famous Blair Street Underground Vaults
- Reach the 287th and final step of the Scott Monument and say "wow" as you gaze across the views of Central Edinburgh
- Find Escaped Animals, a sculpture by Julian "iconic Blur album cover" Opie, on the modern art walking trail at the National Galleries of Scotland
- Sip a wee 21-year-old dram at the end of the Scotch Whisky Experience, home to the world's largest collection of Scotch whisky
- Spot a floating shark at the National Museum of Scotland
London King's Cross to Edinburgh by train is roughly four and a half hours.
Have you been to Brighton, Birmingham, Manchester, Edinburgh, Glasgow, Cardiff, Liverpool, Newcastle or Bristol? Share your best moments - #AutumnSeason - from these nine cities and we'll publish our favourite in this feature.