Identifying the Opportunity Cost of Critical Habitat Designation under the U.S. Endangered Species Act

We determine the effect of the US Endangered Species Act’s Critical Habitat designation on land use change from 1992 to 2011. We find that the rate of change in developed land (constructed material) and agricultural land is not significantly affected by Critical Habitat designation. Therefore, Sections 7 and 9 of the Endangered Species Act do not appear to be more heavily applied in lands designated as Critical Habitat areas versus lands within listed species’ ranges, but without critical habitat designation. Further, there does not appear to be any extraordinary conservation activity in critical habitat areas; for example, environmental non-profits and land trusts do not appear to be concentrating activity in these areas. Before we conclude that the opportunity cost of Critical Habitat designation is negligible we need to examine the land management impacts of designation

Economic-based projections of future land use in the conterminous United States under alternative policy scenarios

Land-use change significantly contributes to biodiversity loss, invasive species spread, changes in biogeochemical cycles, and the loss of ecosystem services. Planning for a sustainable future requires a thorough understanding of expected land use at the fine spatial scales relevant for modeling many ecological processes and at dimensions appropriate for regional or national-level policy making. Our goal was to construct and parameterize an econometric model of land-use change to project future land use to the year 2051 at a fine spatial scale across the conterminous United States under several alternative land-use policy scenarios. We parameterized the econometric model of land-use change with the National Resource Inventory (NRI) 1992 and 1997 land-use data for 844 000 sample points. Land-use transitions were estimated for five land-use classes (cropland, pasture, range, forest, and urban). We predicted land-use change under four scenarios: business-as-usual, afforestation, removal of agricultural subsidies, and increased urban rents. Our results for the business-as-usual scenario showed widespread changes in land use, affecting 36% of the land area of the conterminous United States, with large increases in urban land (79%) and forest (7%), and declines in cropland (\-16%) and pasture (\-13%). Areas with particularly high rates of land-use change included the larger Chicago area, parts of the Pacific Northwest, and the Central Valley of California. However, while land-use change was substantial, differences in results among the four scenarios were relatively minor. The only scenario that was markedly different was the afforestation scenario, which resulted in an increase of forest area that was twice as high as the business-as-usual scenario. Land-use policies can affect trends, but only so much. The basic economic and demographic factors shaping land-use changes in the United States are powerful, and even fairly dramatic policy changes, showed only moderate deviations from the business-as-usual scenario. Given the magnitude of predicted land-use change, any attempts to identify a sustainable future or to predict the effects of climate change will have to take likely land-use changes into account. Econometric models that can simulate land-use change for broad areas with fine resolution are necessary to predict trends in ecosystem service provision and biodiversity persistence. © 2012 by the Ecological Society of America

Projected land-use change impacts on ecosystem services in the United States

Providing food, timber, energy, housing, and other goods and services, while maintaining ecosystem functions and biodiversity that underpin their sustainable supply, is one of the great challenges of our time. Understanding the drivers of land-use change and how policies can alter land-use change will be critical to meeting this challenge. Here we project land-use change in the contiguous United States to 2051 under two plausible baseline trajectories of economic conditions to illustrate how differences in underlying market forces can have large impacts on land-use with cascading effects on ecosystem services and wildlife habitat. We project a large increase in croplands (28.2 million ha) under a scenario with high crop demand mirroring conditions starting in 2007, compared with a loss of cropland (11.2 million ha) mirroring conditions in the 1990s. Projected land-use changes result in increases in carbon storage, timber production, food production from increased yields, and \u3e10% decreases in habitat for 25% of modeled species. We also analyze policy alternatives designed to encourage forest cover and natural landscapes and reduce urban expansion. Although these policy scenarios modify baseline land-use patterns, they do not reverse powerful underlying trends. Policy interventions need to be aggressive to significantly alter underlying land-use change trends and shift the trajectory of ecosystem service provision

Economic-based Projections Of Future Land Use In The Conterminous United States Under Alternative Policy Scenarios

The article presents a study which constructs and parameterizes an econometric model of land-use change to project future land use to the year 2051 at a fine spatial scale across the conterminous U.S. under several alternative land-use policy scenarios. It parameterizes the econometric model of land-use change with the National Resource Inventory (NRI) 1992 and 1997 land-use data for 844 000 sample points

Pre-treatment tumour perfusion parameters and initial RECIST response do not predict long-term survival outcomes for patients with head and neck squamous cell carcinoma treated with induction chemotherapy

<p>Tumour plasma perfusion (F_p) in relation to overall survival (a), disease specific survival (b) and locoregional control (c). Nodal plasma perfusion in relation to survival (d) disease specific survival (e) and locoregional control (f).</p

Effects of different segmentation methods on geometric morphometric data collection from primate skulls

1. An increasing number of studies are analysing the shapes of objects using geometric morphometrics with tomographic data, which are often segmented and transformed to three‐dimensional (3D) surface models before measurement. This study aimed to evaluate the effects of different image segmentation methods on geometric morphometric data collection using computed tomography data collected from non‐human primate skulls. 2. Three segmentation methods based on a visually selected threshold, a half‐maximum height protocol and a gradient and watershed algorithm were compared. For each method, the efficiency of surface reconstruction, the accuracy of landmark placement and the level of variation in shape and size compared with various levels of biological variation were evaluated. 3. The visual‐based method inflated the surface in high‐density anatomical regions, whereas the half‐maximum height protocol resulted in a large number of artificial holes and erosion. However, the gradient‐based method mitigated these issues and generated the most efficient surface model. The segmentation method used had a much smaller effect on shape and size variation than interspecific and inter‐individual differences. However, this effect was statistically significant and not negligible when compared with intra‐individual (fluctuating asymmetric) variation. 4. Although the gradient‐based method is not widely used in geometric morphometric analyses, it may be one of promising options for reconstructing 3D surfaces. When evaluating small variations, such as fluctuating asymmetry, care should be taken around combining 3D data that were obtained using different segmentation methods

Perspectives on Risk Perceptions

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/72341/1/j.1539-6924.1981.tb01409.x.pd

Projected land-use change impacts on ecosystem services in the U.S.

Providing food, timber, energy, housing, and other goods and services, while maintaining ecosystem functions and biodiversity that underpin their sustainable supply, is one of the great challenges of our time. Understanding the drivers of land-use change and how policies can alter land-use change will be critical to meeting this challenge. Here we project land-use change in the contiguous U.S. to 2051 under two plausible baseline trajectories of economic conditions to illustrate how differences in underlying market forces can have large impacts on land-use with cascading effects on ecosystem services and wildlife habitat. We project a large increase in croplands (28.2 million ha) under a scenario with high crop demand mirroring conditions starting in 2007, compared to a loss of cropland (11.2 million ha) mirroring conditions in the 1990s. Projected land-use changes result in increases in carbon storage, timber production, food production from increased yields, and >10% decreases in habitat for 25% of modeled species. We also analyze policy alternatives designed to encourage forest cover, natural landscapes, and reduce urban expansion. Although these policy scenarios modify baseline land-use patterns, they do not reverse powerful underlying trends. Policy interventions need to be aggressive to significantly alter underlying land-use change trends and shift the trajectory of ecosystem service provision.Keywords: econometric model, land-use change, ecosystem services, food production, biodiversity, timber, carbonThis is an author's peer-reviewed final manuscript, as accepted by the publisher. The published article is copyrighted by [publisher] and can be found at: http://www.pnas.org/

The use of the Kalman filter in the automated segmentation of EIT lung images

In this paper, we present a new pipeline for the fast and accurate segmentation of impedance images of the lungs using electrical impedance tomography (EIT). EIT is an emerging, promising, non-invasive imaging modality that produces real-time, low spatial but high temporal resolution images of impedance inside a body. Recovering impedance itself constitutes a nonlinear ill-posed inverse problem, therefore the problem is usually linearized, which produces impedance-change images, rather than static impedance ones. Such images are highly blurry and fuzzy along object boundaries. We provide a mathematical reasoning behind the high suitability of the Kalman filter when it comes to segmenting and tracking conductivity changes in EIT lung images. Next, we use a two-fold approach to tackle the segmentation problem. First, we construct a global lung shape to restrict the search region of the Kalman filter. Next, we proceed with augmenting the Kalman filter by incorporating an adaptive foreground detection system to provide the boundary contours for the Kalman filter to carry out the tracking of the conductivity changes as the lungs undergo deformation in a respiratory cycle. The proposed method has been validated by using performance statistics such as misclassified area, and false positive rate, and compared to previous approaches. The results show that the proposed automated method can be a fast and reliable segmentation tool for EIT imaging