Rural and Suburban Sprawl in the U.S. Midwest from 1940 to 2000 and Its Relation to Forest Fragmentation

Abstract:  Housing growth and its environmental effects pose major conservation challenges. We sought to (1) quantify spatial and temporal patterns of housing growth across the U.S. Midwest from 1940–2000, (2) identify ecoregions strongly affected by housing growth, (3) assess the extent to which forests occur near housing, and (4) relate housing to forest fragmentation. We used data from the 2000 U.S. Census to derive fine-scale backcasts of decadal housing density. Housing data were integrated with a 30-m resolution U.S. Geological Survey land cover classification. The number of housing units in the Midwest grew by 146% between 1940 and 2000. Spatially, housing growth was particularly strong at the fringe of metropolitan areas (suburban sprawl) and in nonmetropolitan areas (rural sprawl) that are rich in natural amenities such as lakes and forests. The medium-density housing (4–32 housing units/km²) category increased most in area. Temporally, suburban housing growth was especially high in the post-World War II decades. Rural sprawl was highest in the 1970s and 1990s. The majority of midwestern forests either contained or were near housing. Only 14.8% of the region's forests were in partial block groups with no housing. Housing density was negatively correlated with the amount of interior forest. The widespread and pervasive nature of sprawl shown by our data is cause for conservation concern. Suburban sprawl has major environmental impacts on comparatively small areas because of the high number of housing units involved. In contrast, rural sprawl affects larger areas but with less intensity because associated housing densities are lower. The environmental effects per house, however, are likely higher in the case of rural sprawl because it occurs in less-altered areas. Conservation efforts will need to address both types of sprawl to be successful.     

Importance of Accounting for Detection Heterogeneity When Estimating Abundance: the Case of French Wolves

Abstract: Assessing conservation strategies requires reliable estimates of abundance. Because detecting all individuals is most often impossible in free‐ranging populations, estimation procedures have to account for a <1 detection probability. Capture–recapture methods allow biologists to cope with this issue of detectability. Nevertheless, capture–recapture models for open populations are built on the assumption that all individuals share the same detection probability, although detection heterogeneity among individuals has led to underestimating abundance of closed populations. We developed multievent capture–recapture models for an open population and proposed an associated estimator of population size that both account for individual detection heterogeneity (IDH). We considered a two‐class mixture model with weakly and highly detectable individuals to account for IDH. In a noninvasive capture–recapture study of wolves we based on genotypes identified in feces and hairs, we found a large underestimation of population size (27% on average) occurred when IDH was ignored.