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Habitat Units (habitat + unit)
Selected AbstractsA pragmatic approach to estimating the distributions and spatial requirements of the medium- to large-sized mammals in the Cape Floristic Region, South AfricaDIVERSITY AND DISTRIBUTIONS, Issue 1-2 2001A. F. Boshoff Conservation planning in the Cape Floristic Region, a recognized world plant diversity hotspot, required systematic information on the estimated distributions and spatial requirements of the medium- to large-sized mammals within each of 102 Broad Habitat Units delineated according to key biophysical parameters. As a consequence of a general lack of data, we derived a pragmatic approach for obtaining estimates of these two parameters. Distribution estimates were based on a combination of a literature survey (with emphasis on early texts) and the ecological requirements of the species. Spatial requirement estimates were derived from a simple spreadsheet model that is based on forage availability estimates and the metabolic requirements of the mammals in question. Our analysis incorporated adaptations of the agriculture-based Large Stock Unit or Animal Unit approach. The predictions of the model were tested by comparing them with actual density data. The outcomes provided realistic estimates of the two parameters. However, they should be considered as testable hypotheses and the concept of adaptive management , or management by hypothesis , must apply. Examples of the outcomes are provided in the form of maps and tables. [source] Density-dependent growth of young-of-the-year Atlantic salmon Salmo salar in Catamaran Brook, New BrunswickJOURNAL OF ANIMAL ECOLOGY, Issue 3 2005I. IMRE Summary 1While density-dependent mortality and emigration have been widely reported in stream salmonid populations, density-dependent growth is less frequently detected. A recent study suggests that density-dependent growth in stream salmonids occurs at low densities, whereas density-dependent mortality and emigration occur at high densities. 2To test the hypothesis that density-dependent growth occurs primarily at low rather than at high densities, we examined the relationship between average fork length and population density of young-of-the-year (YOY) Atlantic salmon at the end of the growing season using a 10-year data set collected on Catamaran Brook, New Brunswick. We tested whether (1) average body size decreases with increasing density; (2) the effect of density on average body size is greatest at low densities; (3) growth rate will decrease most rapidly at low effective densities [,(fork length)2]; (4) density-dependent growth is weaker over space than over time; and (5) the strength of density-dependent growth increases with the size of the habitat unit (i.e. spatial scale) when compared within years, but not between years. 3There was a strong negative relationship between the average body size and population density of YOY Atlantic salmon in the autumn, which was best described by a negative power curve. Similarly, a negative power curve provided the best fit to the relationship between average body size and effective density. Most of the variation in average body size was explained by YOY density, with year, location and the density of 1+ and 2+ salmon accounting for a minor proportion of the variation. 4The strength of density-dependent growth did not differ significantly between comparisons over space vs. time. Consistent with the last prediction, the strength of density-dependent growth increased with increasing spatial scale in the within-year, but not in the between-year comparisons. 5The effect of density on growth was strongest at low population densities, too low to expect interference competition. Stream salmonid populations may be regulated by two mechanisms: density-dependent growth via exploitative competition at low densities, perhaps mediated by predator-induced reductions in drift rate, and density-dependent mortality and emigration via interference competition at high densities. [source] Using geophysical information to define benthic habitats in a large riverFRESHWATER BIOLOGY, Issue 1 2006DAVID L. STRAYER Summary 1. Most attempts to describe the distribution of benthic macroinvertebrates in large rivers have used local (grab-scale) assessments of environmental conditions, and have had limited ability to account for spatial variation in macroinvertebrate populations. 2. We tested the ability of a habitat classification system based on multibeam bathymetry, side-scan sonar, and chirp sub-bottom seismics to identify large-scale habitat units (,facies') and account for macroinvertebrate distribution in the Hudson River, a large tidal river in eastern New York. 3. Partial linear regression analysis showed that sediment facies were generally more effective than local or positional variables in explaining various aspects of the macroinvertebrate community (community structure, density of all invertebrates, density of fish forage, density of a pest species ,Dreissena polymorpha). 4. Large-scale habitats may be effective at explaining macroinvertebrate distributions in large rivers because they are integrative and describe habitat at the spatial scales of dominant controlling processes. [source] Predicting abundance from occupancy: a test for an aggregated insect assemblageJOURNAL OF ANIMAL ECOLOGY, Issue 3 2003M. Warren Summary 1The ubiquitous, positive abundance-occupancy relationship is of potential value to conservation and pest management because of the possibility of using it to predict species abundance from occupancy measures. 2He & Gaston (2000a) developed a model, and a parameterization method, for the prediction of abundance from occupancy based on the negative binomial distribution. There are to date few empirical tests of either the estimation method or model. Here we conduct such a test in a field-based mesocosm experiment using a Drosophilidae assemblage associated with decaying fruit. 3With individual (and groups of) fruit as minimum mapping units, abundance estimates derived using the parameterization method of the He-Gaston model differed significantly from measured values, and were least accurate for the most abundant species. 4Substitution of k -values corrected for species density in the model did not improve abundance predictions significantly. However, substitution of k -values calculated directly from the negative binomial distribution yielded highly accurate abundance predictions. 5Although the distribution of fly species did not deviate significantly from the negative binomial distribution, and the finest possible minimum mapping units were used (individual fruit), the parameterization method in the He-Gaston model consistently underestimated the abundance of species in the assemblage because individuals were very highly aggregated within fruit. 6Because of its potential importance, this model and parameterization method require further exploration at fine scales, commonly represented by individual habitat units, for highly aggregated species. The incorporation of spatially explicit information may provide a means of improving abundance predictions in this regard. [source] | ||||||||||