Depauperate Avifauna in Plantations Compared to Forests and Exurban Areas

Native forests are shrinking worldwide, causing a loss of biological diversity. Our ability to prioritize forest conservation actions is hampered by a lack of information about the relative impacts of different types of forest loss on biodiversity. In particular, we lack rigorous comparisons of the effects of clearing forests for tree plantations and for human settlements, two leading causes of deforestation worldwide. We compared avian diversity in forests, plantations and exurban areas on the Cumberland Plateau, USA, an area of global importance for biodiversity. By combining field surveys with digital habitat databases, and then analyzing diversity at multiple scales, we found that plantations had lower diversity and fewer conservation priority species than did other habitats. Exurban areas had higher diversity than did native forests, but native forests outscored exurban areas for some measures of conservation priority. Overall therefore, pine plantations had impoverished avian communities relative to both native forests and to exurban areas. Thus, reports on the status of forests give misleading signals about biological diversity when they include plantations in their estimates of forest cover but exclude forested areas in which humans live. Likewise, forest conservation programs should downgrade incentives for plantations and should include settled areas within their purview

Effects of gender and season on spatial and temporal patterns of deer-vehicle collisions

White-tailed deer (Odocoileus virginianus) are a serious accident hazard, especially in suburban communities with high deer densities. Such areas are becoming more common as deer populations continue to grow throughout the northeastern United States. This study analyzed deer-vehicle collision data collected from police reports in Connecticut for 2000, 2001 and 2002. The purpose of this project was to integrate the use of standard crime mapping tools, multi-temporal remotely sensed vegetation imagery, human infrastructure, and the behavioral aspect of white-tailed deer to create a spatially explicit model of gender-specific deer-vehicle accident probabilities. We found marked differences between number, location, and seasonality of male and female accidents. Through most of the year, the number of males and females involved in accidents were relative to their proportion in the population. However, during the breeding season, there were a higher proportion of males involved in accidents. The spatial distribution of accidents involving deer also varied by season and sex – outside of the breeding season, accidents involving male deer were concentrated in a few key locations in the state. The difference in the spatial location of male and female accidents could be the result of resource partitioning exhibited by the species, with males occupying broader ranges in peripheral habitats. This model can be used to predict high risk areas as they change over the different seasons and design warning programs and adaptive education to these target areas

Stakeholder partnerships as collaborative policymaking: Evaluation criteria applied to watershed management in California and Washington

Public policymaking and implementation in the United States are increasingly handled through local, consensus-seeking partnerships involving most affected stakeholders. This paper formalizes the concept of a stakeholder partnership, and proposes techniques for using interviews, surveys, and documents to measure each of six evaluation criteria. Then the criteria are applied to 44 watershed partnerships in California and Washington. The data suggest that each criterion makes a unique contribution to the overall evaluation, and together the criteria reflect a range of partnership goals-both short-term and long-term, substantive and instrumental. Success takes time-frequently about 48 months to achieve major milestones, such as formal agreements and implementation of restoration, education, or monitoring projects. Stakeholders perceive that their partnerships have been most effective at addressing local problems and at addressing serious problems-not just uncontroversial issues, as previously hypothesized. On the other hand, they perceive that partnerships have occasionally aggravated problems involving the economy, regulation, and threats to property rights. © 2002 by the Association for Public Policy Analysis and Management.

Effects of gender and seasons on spatial and temporal patterns of deer-vehicle collisions. Road Ecology

Abstract White-tailed deer (Odocoileus virginianus) are a serious accident hazard, especially in suburban communities with high deer densities. Such areas are becoming more common as deer populations continue to grow throughout the northeastern United States. This study analyzed deer-vehicle collision data collected from police reports in Connecticut for 2000, 2001 and 2002. The purpose of this project was to integrate the use of standard crime mapping tools, multi-temporal remotely sensed vegetation imagery, human infrastructure, and the behavioral aspect of white-tailed deer to create a spatially explicit model of gender-specific deer-vehicle accident probabilities. We found marked differences between number, location, and seasonality of male and female accidents. Through most of the year, the number of males and females involved in accidents were relative to their proportion in the population. However, during the breeding season, there were a higher proportion of males involved in accidents. The spatial distribution of accidents involving deer also varied by season and sexoutside of the breeding season, accidents involving male deer were concentrated in a few key locations in the state. The difference in the spatial location of male and female accidents could be the result of resource partitioning exhibited by the species, with males occupying broader ranges in peripheral habitats. This model can be used to predict high risk areas as they change over the different seasons and design warning programs and adaptive education to these target areas. Chapter 10 478 ICOET Abstract: White-tailed deer (Odocoileus virginianus) are a serious accident hazard, especially in suburban communities with high deer densities. Such areas are becoming more common as deer populations continue to grow throughout the northeastern United States. This study analyzed deer-vehicle collision data collected from police reports in Connecticut for 2000, 2001 and 2002. The purpose of this project was to integrate the use of standard crime mapping tools, multi-temporal remotely sensed vegetation imagery, human infrastructure, and the behavioral aspect of white-tailed deer to create a spatially explicit model of gender-specific deer-vehicle accident probabilities. We found marked differences between number, location, and seasonality of male and female accidents. Through most of the year, the number of males and females involved in accidents were relative to their proportion in the population. However, during the breeding season, there were a higher proportion of males involved in accidents. The spatial distribution of accidents involving deer also varied by season and sex -outside of the breeding season, accidents involving male deer were concentrated in a few key locations in the state. The difference in the spatial location of male and female accidents could be the result of resource partitioning exhibited by the species, with males occupying broader ranges in peripheral habitats. This model can be used to predict high risk areas as they change over the different seasons and design warning programs and adaptive education to these target areas

Life history characteristics of birds in each habitat class.

<div><p>(a) Numbers of species nesting in different nest site types in each habitat class, (b) Numbers of species with different migratory patterns in each habitat class.</p> <p>(E = early pine plantation, M = mid-aged pine plantation, L = late pine plantation, N = native forest, X = Exurban areas, T = thinned native forest.)</p></div

Indices of bird abundance for six habitat classes.

<div><p>Filled bars show means and standard errors of indices of abundance calculated by counting all birds within 50 m of each count center.</p> <p>Habitat classes with the same letter are not significantly different from one another in a Tukey HSD multiple means comparison calculated using this data.</p> <p>Open bars show estimated abundance with 95% confidence intervals from DISTANCE software using all birds detected up to 150 m.</p> <p>Numbers above bars show the percentage difference between the DISTANCE estimate and the estimate made by counting birds within 50 m of count centers.</p> <p>(E = early pine plantation, M = mid-aged pine plantation, L = late pine plantation, N = native forest, X = Exurban areas, T = thinned native forest.)</p></div

Detrended correspondence analysis of bird communities calculated at the scale of transects.

<div><p>Each point represents the position in ordination space of the bird community detected at one transect.</p> <p>The two axes show the relative position of each transect in the multi-dimensional space defined by the species found on each transect.</p> <p>Thus transects with similar bird communities cluster together on the graph.</p> <p>The first axis (DCA 1) is the one along which most of the variation in the ordination space is arranged (eigenvalue = 0.56), the second axis (DCA 2) is the second most important axis through the ordination space (eigenvalue = 0.44).</p></div

Summary of Pearson correlations between landscape metrics and bird community characteristics.

<div><p>Only statistically significant correlations are shown (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0000063#pone-0000063-t003" target="_blank">Table 3</a> for listing of all correlations, regardless of significance).</p> <p>All buffers were calculated around the locations of point counts in exurban areas.</p></div

Species evenness in each habitat class.

<div><p>Evenness is shown by the probability of interspecific encounter (PIE, shown by thick lines; thin lines show 95% confidence intervals).</p> <p>PIE controls for both sampling effort and bird density, and uses repeated re-sampling of the data to calculate the probability that the next bird sampled will be of a different species.</p> <p>Therefore, high PIE values indicate high species evenness.</p> <p>See “caveats” section of Discussion for an analysis of how detectability differences might influence the curves in this figure.</p></div

Do mangroves provide an effective barrier to storm surges?

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