John P. Fay

Climate Change, Elevational Range Shifts, and Bird Extinctions

Limitations imposed on species ranges by the climatic, ecological, and physiological effects of elevation are important determinants of extinction risk. We modeled the effects of elevational limits on the extinction risk of landbirds, 87% of all bird species. Elevational limitation of range size explained 97% of the variation in the probability of being in a World Conservation Union category of extinction risk. Our model that combined elevational ranges, four Millennium Assessment habitat-loss scenarios, and an intermediate estimate of surface warming of 2.8 degrees C, projected a best guess of 400-550 landbird extinctions, and that approximately 2150 additional species would be at risk of extinction by 2100. For Western Hemisphere landbirds, intermediate extinction estimates based on climate-induced changes in actual distributions ranged from 1.3% (1.1 degrees C warming) to 30.0% (6.4 degrees C warming) of these species. Worldwide, every degree of warming projected a nonlinear increase in bird extinctions of about 100-500 species. Only 21% of the species predicted to become extinct in our scenarios are currently considered threatened with extinction. Different habitat-loss and surface-warming scenarios predicted substantially different futures for landbird species. To improve the precision of climate-induced extinction estimates, there is an urgent need for high-resolution measurements of shifts in the elevational ranges of species. Given the accelerating influence of climate change on species distributions and conservation, using elevational limits in a tested, standardized, and robust manner can improve conservation assessments of terrestrial species and will help identify species that are most vulnerable to global climate change. Our climate-induced extinction estimates are broadly similar to those of bird species at risk from other factors, but these estimates largely involve different sets of species.

Potential carbon mitigation and income in developing countries from changes in use and management of agricultural and forest lands.

The many opportunities for mitigating atmospheric carbon emissions in developing countries include reforesting degraded lands, implementing sustainable agricultural practices on existing lands and slowing tropical deforestation. This analysis shows that over the next 10 years, 48 major tropical and subtropical developing countries have the potential to reduce the atmospheric carbon burden by about 2.3 billion tonnes of carbon. Given a central price of

Linking spatial patterns of bird and butterfly species richness with Landsat TM derived NDVI

10 per tonne of carbon and a discount rate of 3%, this mitigation would generate a net present value of about

How Many Mountains Can We Mine? Assessing the Regional Degradation of Central Appalachian Rivers by Surface Coal Mining

16.8 billion collectively for these countries. Achieving these potentials would require a significant global effort, covering more than 50 million hectares of land, to implement carbon-friendly practices in agriculture, forest and previously forested lands. These estimates of host-country income potentials do not consider that outside financial investment may or may not be available. Our calculations take no account of the additional benefits of carbon sequestration in forest soils undergoing reforestation, increased use of biomass and reduced use of fossil-fuel inputs and reduced agricultural emissions. In all events, realizing these incomes would necessitate substantially greater policy support and investment in sustainable land uses than is currently the case.

A quantitative analysis of forest fragmentation in Los Tuxtlas, southeast Mexico: patterns and implications for conservation

The ability to predict spatial patterns of species richness using a few easily measured environmental variables would facilitate timely evaluation of potential impacts of anthropogenic and natural disturbances on biodiversity and ecosystem functions. Two common hypotheses maintain that faunal species richness can be explained in part by either local vegetation heterogeneity or primary productivity. Although remote sensing has long been identified as a potentially powerful source of information on the latter, its principal application to biodiversity studies has been to develop classified vegetation maps at relatively coarse resolution, which then have been used to estimate animal diversity. Although classification schemes can be delineated on the basis of species composition of plants, these schemes generally do not provide information on primary productivity. Furthermore, the classification procedure is a time- and labour-intensive process, yielding results with limited accuracy. To meet decision-making needs and to develop land management strategies, more efficient methods of generating information on the spatial distribution of faunal diversity are needed. This article reports on the potential of predicting species richness using single-date Normalized Difference Vegetation Index (NDVI) derived from Landsat Thematic Mapper (TM). We use NDVI as an indicator of vegetation productivity, and examine the relationship of three measures of NDVI—mean, maximum, and standard deviation—with patterns of bird and butterfly species richness at various spatial scales. Results indicate a positive correlation, but with no definitive functional form, between species richness and productivity. The strongest relationships between species richness of birds and NDVI were observed at larger sampling grains and extent, where each of the three NDVI measures explained more than 50% of the variation in species richness. The relationship between species richness of butterflies and NDVI was strongest over smaller grains. Results suggest that measures of NDVI are an alternative approach for explaining the spatial variability of species richness of birds and butterflies.

Modelling butterfly species richness using mesoscale environmental variables: model construction and validation for mountain ranges in the Great Basin of western North America

Surface coal mining is the dominant form of land cover change in Central Appalachia, yet the extent to which surface coal mine runoff is polluting regional rivers is currently unknown. We mapped surface mining from 1976 to 2005 for a 19,581 km(2) area of southern West Virginia and linked these maps with water quality and biological data for 223 streams. The extent of surface mining within catchments is highly correlated with the ionic strength and sulfate concentrations of receiving streams. Generalized additive models were used to estimate the amount of watershed mining, stream ionic strength, or sulfate concentrations beyond which biological impairment (based on state biocriteria) is likely. We find this threshold is reached once surface coal mines occupy >5.4% of their contributing watershed area, ionic strength exceeds 308 μS cm(-1), or sulfate concentrations exceed 50 mg L(-1). Significant losses of many intolerant macroinvertebrate taxa occur when as little as 2.2% of contributing catchments are mined. As of 2005, 5% of the land area of southern WV was converted to surface mines, 6% of regional streams were buried in valley fills, and 22% of the regional stream network length drained watersheds with >5.4% of their surface area converted to mines.

Leveraging Big Data Towards Functionally-Based, Catchment Scale Restoration Prioritization

La destruccion del habitat es la principal amenaza para la biodiversidad tropical, por lo que su cuantificacion constituye un aspecto central para la biologia de la conservacion. Usualmente, esta cuantificacion se basa en el calculo de las tasas de deforestacion, ignorando los efectos derivados de la reconfiguracion espacial del habitat remanente postdeforestacion: la fragmentacion. Aqui presentamos un analisis de la fragmentacion en un sitio Neotropical para: (a) proponer un protocolo para su cuantificacion; (b) utilizar tal protocolo para explorar las consecuencias ecologicas de la fragmentacion; y (c) explorar su aplicacion para evaluar la hipotesis de que la heterogeneidad de tamanos de los fragmentos disminuye con la elevacion (indicativo de la accesibilidad del habitat). Calculamos el coeficiente de Gini y la curva de Lorenz para analizar la desigualdad de tamanos de los fragmentos, utilizando un mapa generado con una imagen de satelite; ademas evaluamos el efecto de borde, la forma de los fragmentos y su grado de aislamiento. Encontramos que el bosque remanente incluye 1.005 fragmentos entre 0,5 y 9,356 ha (mediana = 0,89). La desigualdad de tamanos fue considerable (G= 0,928), con una curva de Lorenz muy abatida. El 40% de los fragmentos no tuvo un area libre de un efecto de borde de 30 m de ancho; los fragmentos mas grandes mostraron un desvio considerable con respecto a la forma circular ideal. El 84 % de los fragmentos estuvo aislado, ubicandose mas alla de 500 m de distancia del parche mas grande de bosque y su tamano disminuyo con la distancia. La distribucion de tamanos de los fragmentos vario con la elevacion: el coeficiente de Gini fue menor y la cobertura relativa de bosque fue mayor en la elevacion mas grande, pero la desigualdad fue maxima en una elevacion intermedia. Proponemos que, en vista de los ritmos actuales de deterioro del habitat, la aplicacion de analisis similares puede mejorar nuestras evaluaciones del estado de conservacion de los ecosistemas tropicales y las perspectivas para la conservacion de la biodiversidad

The influence of catchment land use on stream integrity across multiple spatial scales

Abstract If species richness can be modelled as a function of easily quantified environmental variables, the scientific foundation for land-use planning will be strengthened. We used Poisson regression to develop a predictive model of species richness of resident butterflies in the central Great Basin of western North America. Species inventory data and values for 14 environmental variables from 49 locations (canyon segments) in the Toquima Range (Nevada, USA) were used to build the model. We also included squares of the environmental variables to accommodate potential non-linear relationships. Species richness of butterflies was a significant function of elevation and local topographic heterogeneity, with the selected model explaining 57% of the total deviance of species richness. Predictive variables can be derived efficiently from GIS-based data for areas in which species inventories have not yet been conducted. Therefore, in addition to evaluating the ability of the model to explain observed variation in species richness, we generated and tested predictions of species richness for ‘new’ locations that had not been used to build the model. Predictions were effective for five new segments also located in the Toquima Range, but not for 22 new segments in the nearby Shoshone Range. We discuss issues related to generalizability and data quality in model development.

The area requirements of an ecosystem service: crop pollination by native bee communities in California

The persistence of freshwater degradation has necessitated the growth of an expansive stream and wetland restoration industry, yet restoration prioritization at broad spatial extents is still limited and ad-hoc restoration prevails. The River Basin Restoration Prioritization tool has been developed to incorporate vetted, distributed data models into a catchment scale restoration prioritization framework. Catchment baseline condition and potential improvement with restoration activity is calculated for all National Hydrography Dataset stream reaches and catchments in North Carolina and compared to other catchments within the river subbasin to assess where restoration efforts may best be focused. Hydrologic, water quality, and aquatic habitat quality conditions are assessed with peak flood flow, nitrogen and phosphorus loading, and aquatic species distribution models. The modular nature of the tool leaves ample opportunity for future incorporation of novel and improved datasets to better represent the holistic health of a watershed, and the nature of the datasets used herein allow this framework to be applied at much broader scales than North Carolina.