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Abundance Predictions (abundance + prediction)
Selected AbstractsClimate, competition, and the coexistence of island lizardsFUNCTIONAL ECOLOGY, Issue 2 2006L. B. BUCKLEY Summary 1The influence of environmental temperatures and competition combine to determine the distributions of island lizards. Neither a bioenergetic model nor simple models of competition alone can account for the distributions. A mechanistic, bioenergetic model successfully predicts how the abundance of a solitary Anolis lizard species will decline along an island's elevation gradient. However, the abundance trends for sympatric lizards diverge from the predictions of the non-interactive model. 2Here we incorporate competition in the bioenergetic model and examine how different forms of competition modify the temperature-based abundance predictions. 3Applying the bioenergetic model with competition to an island chain tests whether the model can successfully predict on which islands two lizards species will coexist. 4Coexistence is restricted to the two largest islands, which the model predicts have substantially greater carrying capacities than the smaller islands. The model successfully predicts that competition prevents species coexistence on the smallest islands. However, the model predicts that the mid-sized islands are capable of supporting substantial populations of both species. Additional island characteristics, such as habitat diversity, resource availability and temporal disturbance patterns, may prevent coexistence. [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] On testing predictions of species relative abundance from maximum entropy optimisationOIKOS, Issue 4 2010Stephen H. Roxburgh A randomisation test is described for assessing relative abundance predictions from the maximum entropy approach to biodiversity. The null model underlying the test randomly allocates observed abundances to species, but retains key aspects of the structure of the observed communities; site richness, species composition, and trait covariance. Three test statistics are used to explore different characteristics of the predictions. Two are based on pairwise comparisons between observed and predicted species abundances (RMSE, RMSESqrt). The third statistic is novel and is based on community-level abundance patterns, using an index calculated from the observed and predicted community entropies (EDiff). Validation of the test to quantify type I and type II error rates showed no evidence of bias or circularity, confirming the dependencies quantified by Roxburgh and Mokany (2007) and Shipley (2007) have been fully accounted for within the null model. Application of the test to the vineyard data of Shipley et al. (2006) and to an Australian grassland dataset indicated significant departures from the null model, suggesting the integration of species trait information within the maximum entropy framework can successfully predict species abundance patterns. The paper concludes with some general comments on the use of maximum entropy in ecology, including a discussion of the mathematics underlying the Maxent optimisation algorithm and its implementation, the role of absent species in generating biased predictions, and some comments on determining the most appropriate level of data aggregation for Maxent analysis. [source] | ||||||||||