Patterns of Houses and Habitat Loss From 1937 to 1999 in Northern Wisconsin, Usa

Abstract

Rural America is witnessing widespread housing development, which is to the detriment of the environment. It has been suggested to cluster houses so that their disturbance zones overlap and thus cause less habitat loss than is the case for dispersed development. Clustering houses makes intuitive sense, but few empirical studies have quantified the spatial pattern of houses in real landscapes, assessed changes in their patterns over time, and quantified the resulting habitat loss. We addressed three basic questions: (1) What are the spatial patterns of houses and how do they change over time; (2) How much habitat is lost due to houses, and how is this affected by spatial pattern of houses; and (3) What type of habitat is most affected by housing development. We mapped 27 419 houses from aerial photos for five time periods in 17 townships in northern Wisconsin and calculated the terrestrial land area remaining after buffering each house using 100- and 500-m disturbance zones. The number of houses increased by 353% between 1937 and 1999. Ripley's K test showed that houses were significantly clustered at all time periods and at all scales. Due to the clustering, the rate at which habitat was lost (176% and 55% for 100- and 500-m buffers, respectively) was substantially lower than housing growth rates, and most land area was undisturbed (95% and 61% for 100-m and 500-m buffers, respectively). Houses were strongly clustered within 100 m of lakes. Habitat loss was lowest in wetlands but reached up to 60% in deciduous forests. Our results are encouraging in that clustered development is common in northern Wisconsin, and habitat loss is thus limited. However, the concentration of development along lakeshores causes concern, because these may be critical habitats for many species. Conservation goals can only be met if policies promote clustered development and simultaneously steer development away from sensitive ecosystems.

The changes of densities and patterns of roads and rural buildings: a case study on Dongzhi Yuan of the Loess Plateau, China

“…Roads and rural buildings were digitized separately from those images in ERDAS 8.6. Here, roads were characterized by white lines and rural buildings were characterized by black, shaded, or white squares (Gonzalez-Abraham et al 2007b). They were not subclassified due to the lack of more information, although the classification is very important (Kalwij et al 2008).…”

“…The variable of rural building pattern is the proportion of occupied cells per sample with at least one rural building point, and a value closer to 1 represented a higher dispersed pattern. This pattern index was chosen because it can simply describe dispersed pattern, which may cause higher landscape fragmentation at a given density (Theobald et al 1997;Gonzalez-Abraham et al 2007b). Similarly, we used the same two grids and the pattern index, which is just the proportion of occupied cells per sample with at least one road line to describe road patterns.…”

“…We hypothesized that the three environmental factors and their independent variables could be used to predict different densities and patterns of roads and rural buildings (Gustafson et al 2005;Gonzalez-Abraham et al 2007b). For example, topography was supposed to be negatively related to distribution patterns of roads and rural buildings (Liang and Zhao 2001), and land cover was positively related (Table 1).…”

“…Six simple linear regression models with backward selection procedures were used to explore the changes of densities and two spatial patterns (50 and 400 m) of roads and rural buildings as functions of the topography, land cover, and development level in 1979, 1993, and 2005, respectively. The R 2 values of the regression models were used to compare the importance of the three factors in explaining roads and rural buildings, and further to test whether roads were explained better than rural buildings (Gustafson et al 2005;Gonzalez-Abraham et al 2007b). Based on the regression models mentioned above, the t value of each significant variable was used to compare the importance of variables in determining roads and rural buildings (Gustafson et al 2005;Gonzalez-Abraham et al 2007b).…”

“…The R 2 values of the regression models were used to compare the importance of the three factors in explaining roads and rural buildings, and further to test whether roads were explained better than rural buildings (Gustafson et al 2005;Gonzalez-Abraham et al 2007b). Based on the regression models mentioned above, the t value of each significant variable was used to compare the importance of variables in determining roads and rural buildings (Gustafson et al 2005;Gonzalez-Abraham et al 2007b). To satisfy the assumptions of normal distributions for the regression models, the data were log or square root transformed before analysis (Gonzalez-Abraham et al 2007b).…”