Riparian influences on stream fish assemblage structure in urbanizing streams

We assessed the influence of land cover at multiple spatial extents on fish assemblage integrity, and the degree to which riparian forests can mitigate the negative effects of catchment urbanization on stream fish assemblages. Riparian cover (urban, forest, and agriculture) was determined within 30 m buffers at longitudinal distances of 200 m, 1 km, and the entire network upstream of 59 non-nested fish sampling locations. Catchment and riparian land cover within the upstream network were highly correlated, so we were unable to distinguish between those variables. Most fish assemblage variables were related to % forest and % urban land cover, with the strongest relations at the largest spatial extent of land cover (catchment), followed by riparian land cover in the 1-km and 200-m reach, respectively. For fish variables related to urban land cover in the catchment, we asked whether the influence of riparian land cover on fish assemblages was dependent on the amount of urban development in the catchment. Several fish assemblage metrics (endemic richness, endemic:cosmopolitan abundance, insectivorous cyprinid richness and abundance, and fluvial specialist richness) were all best predicted by single variable models with % urban land cover. However, endemic:cosmopolitan richness, cosmopolitan abundance, and lentic tolerant abundance were related to % forest cover in the 1-km stream reach, but only in streams that had <15% catchment urban land cover. In these cases, catchment urbanization overwhelmed the potential mitigating effects of riparian forests on stream fishes. Together, these results suggest that catchment land cover is an important driver of fish assemblages in urbanizing catchments, and riparian forests are important but not sufficient for protecting stream ecosystems from the impacts of high levels of urbanization.     

Effects of stream map resolution on measures of riparian buffer distribution and nutrient retention potential

Riparian ecosystems are interfaces between aquatic and terrestrial environments recognized for their nutrient interception potential in agricultural landscapes. Stream network maps from a broad range of map resolutions have been employed in watershed studies of riparian areas. However, map resolution may affect important attributes of riparian buffers, such as the connectivity between source lands and small stream channels missing in coarse resolution maps. We sought to understand the influence of changing stream map resolution on measures of the river network, near-stream land cover, and riparian metrics. Our objectives were: (1) to evaluate the influence of stream map resolution on measures of the stream network, the character and extent of near-stream zones, and riparian metrics; (2) to compare patterns of variation among different physiographic provinces; and (3) to explore how predictions of nutrient retention potential might be affected by the resolution of a stream map. We found that using fine resolution stream maps significantly increased our estimates of stream order, drainage density, and the proportion of watershed area occurring near a stream. Increasing stream map resolution reduced the mean distance to source areas as well as mean buffer width and increased the frequency of buffer gaps. Measures of percent land cover within 100 m of streams were less sensitive to stream map resolution. Overall, increasing stream map resolution led to reduced estimates of nutrient retention potential in riparian buffers. In some watersheds, switching from a coarse resolution to a fine resolution stream map completely changed our perception of a stream network from well buffered to largely unbuffered. Because previous, broad-scale analyses of riparian buffers used coarse-resolution stream maps, those studies may have overestimated landscape-level buffer prevalence and effectiveness. We present a case study of three watersheds to demonstrate that interactions among stream map resolution and land cover patterns make a dramatic difference in the perceived ability of riparian buffers to ameliorate effects of agricultural activities across whole watersheds. Moreover, stream map resolution affects inferences about whether retention occurs in streams or riparian zones.     

Ownership and soil quality as sources of agricultural land fragmentation in highly fragmented ownership patterns

The relation between landscape structure and its drivers is a central issue in studies of landscape ecology. However, agricultural land fragmentation is dealt with in only a few such studies. We have investigated the effects of ownership and soil quality on agricultural land fragmentation in the highly fragmented ownership patterns that characterize some of the transition countries of Central and Eastern Europe. Using patch-scale spatial data generated from GIS, Minimal Adequate Models, based on ANOVA, were performed to test for the effects of ownership and soil quality patterns on arable land and grassland fragmentation across 483 study areas. The results show that there are important differences in the predictors of fragmentation between arable land and grassland. Grassland fragmentation was found to be associated particularly with ownership fragmentation, whereas arable land fragmentation tended to be driven mainly by soil conditions. A higher proportion of public ownership supports the more frequent appearance of larger patches. We found a significantly positive relationship between natural soil fertility and arable land fragmentation, while there was a strongly negative relationship between natural soil fertility and grassland fragmentation. Soil quality diversity was observed to be the most important driver affecting arable land fragmentation, but only a non-significant driver of grassland fragmentation. The study provides arguments for intervention aimed at reducing the huge differences between the levels of land-ownership and the land-use fragmentation.     

Prediction of multinomial probability of land use change using a bisection decomposition and logistic regression

Land use change is an important research area in landscape ecology and urban development. Prediction of land use change (urban development) provides critical information for making the right policies and management plans in order to maintain and improve ecosystem and city functions. Logistic regression is a widely used method to predict binomial probabilities of land use change when just two responses (change and no-change) are considered. However, in practice, more than two types of change are encountered and multinomial probabilities are therefore needed. The existing methods for predicting multinomial probabilities have limits in building multinomial probability models and are often based on improper assumptions. This is due to the lack of proper methodology and inadequate software. In this study, a procedure has been developed for building models to predict the multinomial probabilities of land use change and urban development. The foundation of this procedure consists of a special bisection decomposition system for the decomposition of multiple-class systems to bi-class systems, conditional probability inference, and logistic regression for binomial probability models. A case study of urban development has been conducted to evaluate this procedure. The evaluation results demonstrated that different samples and bisection decomposition systems led to very similar quality and performance in the developed multinomial probability models, which indicates the high stability of the proposed procedure for this case study.     

Wealth and land use drive the distribution of urban green space in the tropical coastal city of Haikou,China

Urban green spaces (UGSs) provide a plethora of ecosystem services that benefit humans and wildlife within cities. UGS both promotes and is affected by overall urban ecological health, which can be measured by a wide range of indices. In this study, we investigated the distribution patterns and drivers of UGS within the tropical coastal city of Haikou, Hainan, China over the past decade using a combination of remote sensing data and field work. We interpreted Google earth images from 2010 and 2020 to determine the land cover of UGS within Urban Functional Units (UFUs). We collected socioeconomic variables to test if wealth, site age, and land use determine the proportion of UGS in the city. We found that the proportion of UGS in parks was the highest among all UFUs in 2010 and 2020. In general, UGS increased with maintenance frequency and housing price, which are proxies for the luxury effect. However, land use also played a role in UGS distribution across UFUs, especially in transportation areas, recreation and leisure districts, and residential districts. Haikou is similar to other cities in showing a positive luxury effect on the distribution of UGS. Our results highlight the key roles that parks, colleges and universities, and research institute areas have played in maintaining stable green space within Haikou over the past decade. We suggest that relatively recently implemented governmental programs to increase green space elsewhere in the city may not yet have achieved success within the time span that we analyzed.     

Influences of habitat and land cover on fish distributions along a tributary to Lake Ontario,New York

Influences of habitat and land cover on fish distributions were determined along a lentic–lotic gradient along a tributary to Lake Ontario, New York. Nonmetric multidimensional scaling, cluster analysis, and specific characterization methods were used to classify the fish species into five groups based on their similar patterns of distribution, species-specific habitat relationship, and relative abundance observed along the gradient. A stepwise regression approach was used to select the best habitat and land cover variables to explain variations in the distribution pattern of each fish group. Distribution patterns of the five fish groups were significantly explained by either a set of the selected habitat or land cover predictor variables or a combination of both. Of the 10 habitat variables, water depth, current velocity, aquatic plants, algae, woody debris, sand, and rock-bedrock were selected to explain the variations in distribution patterns of one or more fish groups. Of the 16 land cover types, evergreen wetlands, evergreen plantations, successional shrubs, shrub swamps, roads, and urban areas were selected to explain the variations in distribution patterns of at least one fish group.