Geology topics

R.J. O’Connor

Publications and source records attributed to R.J. O’Connor.

4 recordsLinked to original sources

USGS2008

Dispersal leads to spatial autocorrelation in species distributions: A simulation model

Compared to population growth regulated by local conditions, dispersal has been underappreciated as a central process shaping the spatial distribution of populations. This paper asks: (a) which conditions increase the importance of dispersers relative to local recruits in determining population sizes? and (b) how does dispersal influence the spatial distribution patterns of abundances among connected populations? We approached these questions with a simulation model of populations on a coupled lattice with cells of continuously varying habitat quality expressed as carrying capacities. Each cell contained a population with the basic dynamics of density-regulated growth, and was connected to other populations by immigration and emigration. The degree to which dispersal influenced the distribution of population sizes depended most strongly on the absolute amount of dispersal, and then on the potential population growth rate. Dispersal decaying in intensity with distance left close neighbours more alike in population size than distant populations, leading to an increase in spatial autocorrelation. The spatial distribution of species with low potential growth rates is more dependent on dispersal than that of species with high growth rates; therefore, distribution modelling for species with low growth rates requires particular attention to autocorrelation, and conservation management of these species requires attention to factors curtailing dispersal, such as fragmentation and dispersal barriers. ?? 2007 Elsevier B.V. All rights reserved.

Ecological Modelling↗

USGS2006

Effect of dispersal at range edges on the structure of species ranges

Range edges are of particular interest to ecology because they hold key insights into the limits of the realized niche and associated population dynamics. A recent feature of Oikos summarized the state of the art on range edge ecology. While the typical question is what causes range edges, another important question is how range edges influence the distribution of abundances across a species geographic range when dispersal is present. We used a single species population dynamics model on a coupled-lattice to determine the effects of dispersal on peripheral populations as compared to populations at the core of the range. In the absence of resource gradients, the reduced neighborhood and thus lower connectivity or higher isolation among populations at the range edge alone led to significantly lower population sizes in the periphery of the range than in the core. Lower population sizes mean higher extinction risks and lower adaptability at the range edge, which could inhibit or slow range expansions, and thus effectively stabilize range edges. The strength of this effect depended on the potential population growth rate and the maximum dispersal distance. Lower potential population growth rates led to a stronger effect of dispersal resulting in a higher difference in population sizes between the two areas. The differential effect of dispersal on population sizes at the core and periphery of the range in the absence of resource gradients implies that traditional, habitat-based distribution models could result in misleading conclusions about the habitat quality in the periphery. Lower population sizes at the periphery are also relevant to conservation, because habitat removal not only eliminates populations but also creates new edges. Populations bordering these new edges may experience declines, due to their increased isolation. ?? OIKOS.

Oikos↗

USGS2002

Contrasting determinants of abundance in ancestral and colonized ranges of an invasive brood parasite

Avian species distributions are typically regarded as constrained by spatially extensive variables such as climate, habitat, spatial patchiness, and microhabitat attributes. We hypothesized that the distribution of a brood parasite depends as strongly on host distribution patterns as on biophysical factors and examined this hypothesis with respect to the national distribution of the Brown-headed Cowbird (Molothrus ater). We applied a classification and regression (CART) analysis to data from the Breeding Bird Survey (BBS) and the Christmas Bird Count (CBC) and derived hierarchically organized statistical models of the influence of climate and weather, cropping and land use, and host abundance and distribution on the distribution of the Brown-headed Cowbird within the conterminous United States. The model accounted for 47.2% of the variation in cowbird incidence, and host abundance was the top predictor with an R2 of 18.9%. The other predictors identified by the model (crops 15.7%, weather and climate 14.3%, and region 9.6%) fit the ecological profile of this cowbird. We showed that host abundance was independent of these environmental predictors of cowbird distribution. At the regional scale host abundance played a very strong role in determining cowbird abundance in the cowbird?s colonized range east and west of their ancestral range in the Great Plains (26.6%). Crops were not a major predictor for cowbirds in their ancestral range, although they are the most important predictive factor (33%) for the grassland passerines that are the cowbird?s ancestral hosts. Consequently our findings suggest that the distribution of hosts does indeed take precedence over habitat attributes in shaping the cowbird?s distribution at a national scale, within an envelope of constraint set by biophysical factors.

Book chapter↗

USGS1996

Spatial partitioning of environmental correlates of avian biodiversity in the conterminous United States

Classification and regression tree (CART) analysis was used to create hierarchically organized models of the distribution of bird species richness across the conterminous United States. Species richness data were taken from the Breeding Bird Survey and were related to climatic and land use data. We used a systematic spatial grid of approximately 12,500 hexagons, each approximately 640 square kilometres in area. Within each hexagon land use was characterized by the Loveland et al. land cover classification based on Advanced Very High Resolution Radiometer (AVHRR) data from NOAA polar orbiting meteorological satellites. These data were aggregated to yield fourteen land classes equivalent to an Anderson level II coverage; urban areas were added from the Digital Chart of the World. Each hexagon was characterized by climate data and landscape pattern metrics calculated from the land cover. A CART model then related the variation in species richness across the 1162 hexagons for which bird species richness data were available to the independent variables, yielding an R2-type goodness of fit metric of 47.5% deviance explained. The resulting model recognized eleven groups of hexagons, with species richness within each group determined by unique sequences of hierarchically constrained independent variables. Within the hierarchy, climate data accounted for more variability in the bird data, followed by land cover proportion, and then pattern metrics. The model was then used to predict species richness in all 12,500 hexagons of the conterminous United States yielding a map of the distribution of these eleven classes of bird species richness as determined by the environmental correlates. The potential for using this technique to interface biogeographic theory with the hierarchy theory of ecology is discussed. ?? 1996 Blackwell Science Ltd.

Biodiversity Letters↗