Showing papers by "Ralph Charles Mac Nally published in 2001"

Modeling and Predicting Species Occurrence Using Broad-Scale Environmental Variables: an Example with Butterflies of the Great Basin

Abstract: If occurrence of individual species can be modeled as a function of easily quantified environmental variables (e.g., derived from a geographic information system [GIS]) and the predictions of these models are demonstrably successful, then the scientific foundation for management planning will be strengthened. We used Bayesian logistic regression to develop predictive models for resident butterflies in the central Great Basin of western North America. Species inventory data and values for 14 environmental variables from 49 locations (segments of canyons) in the Toquima Range ( Nevada, U.S.A.) were used to build the models. Squares of the environmental variables were also used to accommodate possibly nonmonotonic responses. We obtained statistically significant models for 36 of 56 (64%) resident species of butterflies. The models explained 8–72% of the deviance in occurrence of those species. Each of the independent variables was significant in at least one model, and squared versions of five variables contributed to models. Elevation was included in more than half of the models. Models included one to four variables; only one variable was significant in about half the models. We conducted preliminary tests of two of our models by using an existing set of data on the occurrence of butterflies in the neighboring Toiyabe Range. We compared conventional logistic classification with posterior probability distributions derived from Bayesian modeling. For the latter, we restricted our predictions to locations with a high ( 70%) probability of predicted presence or absence. We will perform further tests after conducting inventories at new locations in the Toquima Range and nearby Shoshone Mountains, for which we have computed environmental variables by using remotely acquired topographic data, digital-terrain and microclimatic models, and GIS computation. Resumen: En la medida en que se pueda modelar la presencia de especies enfuncion de variables ambientales faulmente cuantificables (por ejempto, derivadas de SIG) y las predicciones de estos modelos se puedan de mostrar con exito, el fundamento cientifico para la planeacion del manejo sera reforzada. Empleamos regresion logistica Bayesiana para desarrollar modelos predictivos para mariposas residentes en la region central de la Gran Depresion del oeste de Norteamerica. Usamos datos del inventario de especies y de valores de 14 variables ambientales de 49 localidades (segmentos de canones) en la cordillera Toquima ( Nevada, USA) para construir estos modelos. Los cuadrados de las variables ambientales tambien fueron usados para acomodar posibles respuestas no monotonicas. Obtuvimos modelos estadisticamente significativos para 36 (64%) de 56 especies residentes de mariposas. Estos modelos explican 8-72% de la variabilidad en la presencia de estas especies. Cada una de las variables independientes fue significativa en al menos un modelo y las versiones cuadradas de cinco variables contribuyeron a los modelos. La elevacion se incluyo en mas de la mitad de los modelos. Los modelos incluyeron de 1-4 variables; en casi la mitad de los modelos solo una variable fue significativa. Realizamos pruebas preliminares de dos de los cuatro modelos usando un juego de datos existente sobre la presencia de mariposas en la cordillera vecina de Toiyabe. Comparamos clasificaciones logisticas convencionales contra distribuciones de probabilidad posteriores derivadas de modelos bayesianos. Para estas ultimas, restringimos nuestras predicciones a localidades con un alta ( 70%) probabilidad de predecir presencia o ausencia. Futuras pruebas seran realizadas despues de llevar a cabo inventarios en nuevas localidades de la cordillera Toquima y de la cordillera vecina Shoshone, para las cuales hemos calculado variables ambientales usando datos topograficos adquiridos por percepcion remota, terreno digital y modelos microclimaticos, y calculos de SIG.

Reptiles and habitat fragmentation in the box-ironbark forests of central Victoria, Australia: predictions, compositional change and faunal nestedness

Abstract: There are few published accounts addressing the impact of habitat fragmentation on biodiversity of terrestrial reptiles Here we provide information on the saurofaunas of fragments of box-ironbark forest of central Victoria, south-eastern Australia, gathered over three years Data for fragments of four size classes (10 ha, 20 ha, 40 ha, 80 ha) are compared with similar information for 'reference areas', which are sites of the same range of areas but set within large blocks of continuous forest (>10,000 ha) These comparisons are used to differentiate fragmentation effects from normal species–area relationships Two sets of data are presented One is an 'extensive' set based on 68 fragments and reference areas surveyed by using transect counts and area searches The other 'intensive' set also included pitfall trapping, but was restricted to just 17 fragments and reference areas The reptilian fauna generally was both depauperate and of low abundance Differences in capture-rates and species richness between fragments and reference areas collectively are minor, but there are significant differences in species richness between area classes While fragmentation does not seem to be influential on total numbers and richness, there are important differences in the occurrence of certain species The two most abundant species in reference areas were very rare in fragments, while a number of species were either in much greater numbers or only occurred in fragments Many of the observed distributions were consistent with predictions made prior to the study on differences in vulnerability between species We show that the saurofaunas are 'nested subsets' by area, with strongest nesting apparent in the intensive data-set for reference areas