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Data Layers (data + layer)
Selected AbstractsArchaeological site distribution by geomorphic setting in the southern lower Cuyahoga River Valley, northeastern Ohio: Initial observations from a GIS databaseGEOARCHAEOLOGY: AN INTERNATIONAL JOURNAL, Issue 8 2004Andrew Bauer In this study, we compiled unpublished archival documentation of archaeological site locations from the southern part of the Cuyahoga River Valley in northeastern Ohio, USA, registered at the State of Ohio Historic Preservation Office into a Geographic Information Systems (GIS) database. Using digitized soil shapefiles to generate a geomorphic data layer, we assessed the spatial and temporal distribution of 79 known archaeological sites by landform association. This digital compilation indicates that Woodland period, Late Prehistoric, and Historic sites occur in most geomorphic settings along the river valley. In contrast, Paleoindian and Archaic sites only occur on Wisconsinan cut terraces and in upland interfluve settings, indicating that most of these documented sites are in primary contexts and have not been reworked. We discuss the distribution of archaeological sites in the study region as a function of various factors, including cultural activities, taphonomic processes, landform development, and the nature and extent of the original archaeological surveys. Observed spatial patterns of known sites clearly reflect local geomorphological controls; artifactual contexts from the earlier prehistoric periods are underrepresented in the database. We conclude that additional site surveys, as well as the excavation and documentation of new sites in this part of Ohio, are required to understand local prehistoric economies and to ascertain patterns of culturally mediated land use. © 2004 Wiley Periodicals, Inc. [source] Epidemiologic Analysis of Factors Associated with Local Disappearances of Native Ranid Frogs in ArizonaCONSERVATION BIOLOGY, Issue 2 2008CARMEL L. WITTE análisis de factores de riesgo; declinación de anfibios; declinación de ranas; epidemiología de vida silvestre; métodos de control de casos Abstract:,We examined factors that may independently or synergistically contribute to amphibian population declines. We used epidemiologic case,control methodology to sample and analyze a large database developed and maintained by the Arizona Game and Fish Department that describes historical and currently known ranid frog localities in Arizona, U.S.A. Sites with historical documentation of target ranid species (n= 324) were evaluated to identify locations where frogs had disappeared during the study period (case sites) and locations where frog populations persisted (control sites). Between 1986 and 2003, 117 (36%) of the 324 sites became case sites, of which 105 were used in the analyses. An equal number of control sites were sampled to control for the effects of time. Risk factors, or predictor variables, were defined from environmental data summarized during site surveys and geographic information system data layers. We evaluated risk factors with univariate and multifactorial logistic-regression analyses to derive odds ratios (OR). Odds for local population disappearance were significantly related to 4 factors in the multifactorial model. Disappearance of frog populations increased with increasing elevation (OR = 2.7 for every 500 m, p < 0.01). Sites where disappearances occurred were 4.3 times more likely to have other nearby sites that also experienced disappearances (OR = 4.3, p < 0.01), whereas the odds of disappearance were 6.7 times less (OR = 0.15, p < 0.01) when there was a source population nearby. Sites with disappearances were 2.6 times more likely to have introduced crayfish than were control sites (OR = 2.6, p= 0.04). The identification of factors associated with frog disappearances increases understanding of declines occurring in natural populations and aids in conservation efforts to reestablish and protect native ranids by identifying and prioritizing implicated threats. Resumen:,Examinamos los factores que pueden contribuir independiente o sinérgicamente a la declinación de poblaciones de anfibios. Utilizamos una metodología epidemiológica de control de casos para muestrear y analizar una base de datos desarrollada y mantenida por el Departamento de Caza y Pesca de Arizona que describe las localidades históricas y actuales de ranas en Arizona, E. U. A. Los sitios con documentación histórica de las especies de ránidos (n= 324) fueron evaluados para identificar localidades donde las ranas desaparecieron durante el período de estudio (sitios caso) y localidades donde las poblaciones de ranas persistieron (sitios control). Entre 1986 y 2003, 36% (117) de los 324 sitios se volvieron sitios caso, de los cuales 105 fueron utilizados en los análisis. El mismo número de sitios control fueron muestreados para controlar los efectos del tiempo. Los factores de riesgo, o variables predictivas, fueron definidos a partir de datos ambientales obtenidos de los muestreos en los sitios y de capas de datos de un sistema información geográfica. Evaluamos los factores de riesgo con análisis de regresión logística univariada y multivariada para derivar proporciones de probabilidades (PP). Las probabilidad para la desaparición de una población local estuvo relacionada significativamente con 4 factores en el modelo multifactorial. La desaparición de poblaciones de ranas incrementó con la elevación (PP = 2.7 por cada 500 m, p < 0.01). Los sitios donde ocurrieron las desapariciones fueron 4.3 veces más propensos a estar cerca de otros sitios donde ocurrieron desapariciones (PP = 4.3, p < 0.01), mientras que la probabilidad de desaparición fue 6.7 veces menos (PP = 0.15, p < 0.01) cuando había una población fuente cercana. Los sitios con desapariciones fueron 2.6 veces más propensos a tener langostinos introducidos que los sitios control (PP = 2.6, p= 0.04). La identificación de factores asociados con la desaparición de ranas incrementa el conocimiento de las declinaciones de poblaciones naturales y ayuda a los esfuerzos de conservación para el reestablecimiento y la protección de ránidos nativos mediante la identificación y priorización de las amenazas implicadas. [source] Effects of species and habitat positional errors on the performance and interpretation of species distribution modelsDIVERSITY AND DISTRIBUTIONS, Issue 4 2009Patrick E. Osborne Abstract Aim, A key assumption in species distribution modelling is that both species and environmental data layers contain no positional errors, yet this will rarely be true. This study assesses the effect of introduced positional errors on the performance and interpretation of species distribution models. Location, Baixo Alentejo region of Portugal. Methods, Data on steppe bird occurrence were collected using a random stratified sampling design on a 1-km2 pixel grid. Environmental data were sourced from satellite imagery and digital maps. Error was deliberately introduced into the species data as shifts in a random direction of 0,1, 2,3, 4,5 and 0,5 pixels. Whole habitat layers were shifted by 1 pixel to cause mis-registration, and the cumulative effect of one to three shifted layers investigated. Distribution models were built for three species using three algorithms with three replicates. Test models were compared with controls without errors. Results, Positional errors in the species data led to a drop in model performance (larger errors having larger effects , typically up to 10% drop in area under the curve on average), although not enough for models to be rejected. Model interpretation was more severely affected with inconsistencies in the contributing variables. Errors in the habitat layers had similar although lesser effects. Main conclusions, Models with species positional errors are hard to detect, often statistically good, ecologically plausible and useful for prediction, but interpreting them is dangerous. Mis-registered habitat layers produce smaller effects probably because shifting entire layers does not break down the correlation structure to the same extent as random shifts in individual species observations. Spatial autocorrelation in the habitat layers may protect against species positional errors to some extent but the relationship is complex and requires further work. The key recommendation must be that positional errors should be minimised through careful field design and data processing. [source] Effect of soil and physiographic factors on ecological plant groups in the eastern Elborz mountain rangeland of IranGRASSLAND SCIENCE, Issue 2 2010Mohammadreza Tatian Abstract To investigate the cause of differences among ecological plant groups in the east of the Elborz mountain rangeland, the role of edaphical and topographical characteristics was considered. Two ordination techniques, detrended correspondence analysis (DCA) and canonical correspondence analysis (CCA), were used. The values of slope, aspect, altitude and lithology information were provided by Geographic Information System (GIS), and geomorphological land units were determined by intersection of overlaid data layers. Plant sampling was undertaken within nine land units with similar lithology and altitude but which differed in slope and aspect, using 30 randomly selected 1 m2 plots per land unit. Soil samples were taken from two depths (0,20 and 20,50 cm) in each plot. Organic matter, bulk density, texture, calcium carbonate, total nitrogen and available phosphorus and potassium contents were determined. The results indicated that plant species have different responses to edaphical and topographical parameters. The invader species group had a balanced amount of influence from all soil components and topographic factors, whereas the native grasses were located in productive soils, which typically have a low grazing intensity, such as the north facing slopes. Coniferous bushy trees, cushion plants and some shrub plant groups were found on steep slopes with alkaline soils. The broad-leaved bushy trees plant group was abundant in fine texture soils on low and humid slopes. [source] The influence of spatial errors in species occurrence data used in distribution modelsJOURNAL OF APPLIED ECOLOGY, Issue 1 2008Catherine H Graham Summary 1Species distribution modelling is used increasingly in both applied and theoretical research to predict how species are distributed and to understand attributes of species' environmental requirements. In species distribution modelling, various statistical methods are used that combine species occurrence data with environmental spatial data layers to predict the suitability of any site for that species. While the number of data sharing initiatives involving species' occurrences in the scientific community has increased dramatically over the past few years, various data quality and methodological concerns related to using these data for species distribution modelling have not been addressed adequately. 2We evaluated how uncertainty in georeferences and associated locational error in occurrences influence species distribution modelling using two treatments: (1) a control treatment where models were calibrated with original, accurate data and (2) an error treatment where data were first degraded spatially to simulate locational error. To incorporate error into the coordinates, we moved each coordinate with a random number drawn from the normal distribution with a mean of zero and a standard deviation of 5 km. We evaluated the influence of error on the performance of 10 commonly used distributional modelling techniques applied to 40 species in four distinct geographical regions. 3Locational error in occurrences reduced model performance in three of these regions; relatively accurate predictions of species distributions were possible for most species, even with degraded occurrences. Two species distribution modelling techniques, boosted regression trees and maximum entropy, were the best performing models in the face of locational errors. The results obtained with boosted regression trees were only slightly degraded by errors in location, and the results obtained with the maximum entropy approach were not affected by such errors. 4Synthesis and applications. To use the vast array of occurrence data that exists currently for research and management relating to the geographical ranges of species, modellers need to know the influence of locational error on model quality and whether some modelling techniques are particularly robust to error. We show that certain modelling techniques are particularly robust to a moderate level of locational error and that useful predictions of species distributions can be made even when occurrence data include some error. [source] ORIGINAL ARTICLE: Predicting species distributions from small numbers of occurrence records: a test case using cryptic geckos in MadagascarJOURNAL OF BIOGEOGRAPHY, Issue 1 2007Richard G. Pearson Abstract Aim, Techniques that predict species potential distributions by combining observed occurrence records with environmental variables show much potential for application across a range of biogeographical analyses. Some of the most promising applications relate to species for which occurrence records are scarce, due to cryptic habits, locally restricted distributions or low sampling effort. However, the minimum sample sizes required to yield useful predictions remain difficult to determine. Here we developed and tested a novel jackknife validation approach to assess the ability to predict species occurrence when fewer than 25 occurrence records are available. Location, Madagascar. Methods, Models were developed and evaluated for 13 species of secretive leaf-tailed geckos (Uroplatus spp.) that are endemic to Madagascar, for which available sample sizes range from 4 to 23 occurrence localities (at 1 km2 grid resolution). Predictions were based on 20 environmental data layers and were generated using two modelling approaches: a method based on the principle of maximum entropy (Maxent) and a genetic algorithm (GARP). Results, We found high success rates and statistical significance in jackknife tests with sample sizes as low as five when the Maxent model was applied. Results for GARP at very low sample sizes (less than c. 10) were less good. When sample sizes were experimentally reduced for those species with the most records, variability among predictions using different combinations of localities demonstrated that models were greatly influenced by exactly which observations were included. Main conclusions, We emphasize that models developed using this approach with small sample sizes should be interpreted as identifying regions that have similar environmental conditions to where the species is known to occur, and not as predicting actual limits to the range of a species. The jackknife validation approach proposed here enables assessment of the predictive ability of models built using very small sample sizes, although use of this test with larger sample sizes may lead to overoptimistic estimates of predictive power. Our analyses demonstrate that geographical predictions developed from small numbers of occurrence records may be of great value, for example in targeting field surveys to accelerate the discovery of unknown populations and species. [source] STORMFLOW SIMULATION USING A GEOGRAPHICAL INFORMATION SYSTEM WITH A DISTRIBUTED APPROACH,JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION, Issue 4 2001Zhongbo Yu ABSTRACT: With the increasing availability of digital and remotely sensed data such as land use, soil texture, and digital elevation models (DEMs), geographic information systems (GIS) have become an indispensable tool in preprocessing data sets for watershed hydrologic modeling and post processing simulation results. However, model inputs and outputs must be transferred between the model and the GIS. These transfers can be greatly simplified by incorporating the model itself into the GIS environment. To this end, a simple hydrologic model, which incorporates the curve number method of rainfall-runoff partitioning, the ground-water base-flow routine, and the Muskingum flow routing procedure, was implemented on the GIS. The model interfaces directly with stream network, flow direction, and watershed boundary data generated using standard GIS terrain analysis tools; and while the model is running, various data layers may be viewed at each time step using the full display capabilities. The terrain analysis tools were first used to delineate the drainage basins and stream networks for the Susquehanna River. Then the model was used to simulate the hydrologic response of the Upper West Branch of the Susquehanna to two different storms. The simulated streamflow hydrographs compare well with the observed hydrographs at the basin outlet. [source] | |||||||||||||