Morphological shifts in island-dwelling birds: The roles of generalist foraging and niche expansion

Passerine birds living on islands are usually larger than their mainland counterparts, in terms of both body size and bill size. One explanation for this island rule is that shifts in morphology are an adaptation to facilitate ecological niche expansion. In insular passerines, for instance, increased bill size may facilitate generalist foraging because it allows access to a broader range of feeding niches. Here we use morphologically and ecologically divergent races of white-eyes (Zosteropidae) to test three predictions of this explanation: (1) island populations show a wider feeding niche than mainland populations; (2) island-dwelling populations are made up of individual generalists; and (3) within insular populations there is a positive association between size and degree of foraging generalism. Our results provide only partial support for the traditional explanation. In agreement with the core prediction, island populations of white-eye do consistently display a wider feeding niche than comparative mainland populations. However, observations of individually marked birds reveal that island-dwelling individuals are actually more specialized than expected by chance. Additionally, neither large body size nor large bill size are associated with generalist foraging behavior per se. These latter results remained consistent whether we base our tests on natural foraging behavior or on observations at an experimental tree, and whether we use data from single or multiple cohorts. Taken together, our results suggest that generalist foraging and niche expansion are not the full explanation for morphological shifts in island-dwelling white-eyes. Hence, we review briefly five alternative explanations for morphological divergence in insular populations: environmental determination of morphology, reduced predation pressure, physiological optimization, limited dispersal, and intraspecific dominance.

Ecological-economic modeling for biodiversity management: Potential, pitfalls, and prospects

Ecologists and economists both use models to help develop strategies for biodiversity management. The practical use of disciplinary models, however, can be limited because ecological models tend not to address the socioeconomic dimension of biodiversity management, whereas economic models tend to neglect the ecological dimension. Given these shortcomings of disciplinary models, there is a necessity to combine ecological and economic knowledge into ecological-economic models. It is insufficient if scientists work separately in their own disciplines and combine their knowledge only when it comes to formulating management recommendations. Such an approach does not capture feedback loops between the ecological and the socioeconomic systems. Furthermore, each discipline poses the management problem in its own way and comes up with its own most appropriate solution. These disciplinary solutions, however are likely to be so different that a combined solution considering aspects of both disciplines cannot be found. Preconditions for a successful model-based integration of ecology and economics include (1) an in-depth knowledge of the two disciplines, (2) the adequate identification and framing of the problem to be investigated, and (3) a common understanding between economists and ecologists of modeling and scale. To further advance ecological-economic modeling the development of common benchmarks, quality controls, and refereeing standards for ecological-economic models is desirable.

Gene organization of the dnaA region of Wolbachia

The dnaA region of Wolbachia, an intracellular bacterial parasite of insects, is unique. A glnA cognate was found upstream of the dnaA gene, while neither of the two open reading frames detected downstream of dnaA has any homologue in the database. This unusual gene arrangement may reflect requirements associated with the unique ecological niche this agent occupies.

A sponge/dinoflagellate association in the haplosclerid sponge Haliclona sp.: cellular origin of cytotoxic alkaloids by Percoll density gradient fractionation

Light-microscopic and electron-microscopic studies of the tropical marine sponge Haliclona sp. (Or der: Haplosclerida Family: Haliclonidae) from Heron Island, Great Barrier Reef, have revealed that this sponge is characterized by the presence of dinoflagellates and by nematocysts. The dinoflagellates are 7-10 mu m in size, intracellular, and contain a pyrenoid with a single stalk, whereas the single chloroplast is branched, curved, and lacks grana. Mitochondria are present, and the nucleus is oval and has distinct chromosomal structure. The dinoflagellates are morphologically similar to Symbiodinium microadriaticum, the common intracellular symbiont of corals, although more detailed biochemical and molecular studies are required to provide a precise taxonomic assignment. The major sponge cell types found in Haliclona sp, are spongocytes, choanocytes, and archaeocytes; groups of dinoflagellates are enclosed within large vacuoles in the archaeocytes. The occurrence of dinoflagellates in marine sponges has previously been thought to be restricted to a small group of sponges including the excavating hadromerid sponges; the dinoflagellates in these sponges are usually referred to as symbionts. The role of the dinoflagellates present in Haliclona sp. as a genuine symbiotic partner requires experimental investigation. The sponge grows on coral substrates, from which it may acquire the nematocysts, and shows features, such as mucus production, which are typical of some excavating sponges. The cytotoxic alkaloids, haliclonacyclamines A and B, associated with Haliclona sp. are shown by Percoll density gradient fractionation to be localized within the sponge cells rather than the dinoflagellates. The ability to synthesize bioactive compounds such as the haliclonacyclamines may help Haliclona sp. to preserve its remarkable ecological niche.

The anammox case - A new experimental manifesto for microbiological eco-physiology

The anaerobic ammonium oxidation process is a new process for ammonia removal from wastewater. It is also a new microbial physiology that was previously believed to be impossible. The identification of Candidatus Brocadia anammoxidans and its relatives as the responsible bacteria was only possible with the development of a new experimental approach. That approach is the focus of this paper. The approach is a modernisation of the Winogradsky/Beyerinck strategy of selective enrichment and is based on the introduction of the molecular toolbox and modern bioreactor engineering to microbial ecology. It consists of five steps: (1) postulation of an ecological niche based on thermodynamic considerations and macro-ecological field data; (2) engineering of this niche into a laboratory bioreactor for enrichment culture; (3) black-box physiological characterisation of the enrichment culture as a whole; (4) phylogenetic characterisation of the enriched community using molecular tools; (5) physical separation of the dominant members of the enrichment culture using gradient centrifugation and the identification of the species of interest in accordance with Koch's postulates; (6) verification of the in situ importance of these species in the actual ecosystems. The power of this approach is illustrated with a case study: the identification of the planctomycetes responsible for anaerobic ammonium oxidation. We argue that this was impossible using molecular ecology or conventional 'cultivation based techniques' alone. We suggest that the approach might also be used for the microbiological study of many interesting microbes such as anaerobic methane oxidisers.

Modelling pre-clearing vegetation distribution using GIS-integrated statistical, ecological and data models: A case study from the wet tropics of Northeastern Australia

Traditional vegetation mapping methods use high cost, labour-intensive aerial photography interpretation. This approach can be subjective and is limited by factors such as the extent of remnant vegetation, and the differing scale and quality of aerial photography over time. An alternative approach is proposed which integrates a data model, a statistical model and an ecological model using sophisticated Geographic Information Systems (GIS) techniques and rule-based systems to support fine-scale vegetation community modelling. This approach is based on a more realistic representation of vegetation patterns with transitional gradients from one vegetation community to another. Arbitrary, though often unrealistic, sharp boundaries can be imposed on the model by the application of statistical methods. This GIS-integrated multivariate approach is applied to the problem of vegetation mapping in the complex vegetation communities of the Innisfail Lowlands in the Wet Tropics bioregion of Northeastern Australia. The paper presents the full cycle of this vegetation modelling approach including sampling sites, variable selection, model selection, model implementation, internal model assessment, model prediction assessments, models integration of discrete vegetation community models to generate a composite pre-clearing vegetation map, independent data set model validation and model prediction's scale assessments. An accurate pre-clearing vegetation map of the Innisfail Lowlands was generated (0.83r(2)) through GIS integration of 28 separate statistical models. This modelling approach has good potential for wider application, including provision of. vital information for conservation planning and management; a scientific basis for rehabilitation of disturbed and cleared areas; a viable method for the production of adequate vegetation maps for conservation and forestry planning of poorly-studied areas. (c) 2006 Elsevier B.V. All rights reserved.

Swapping space for time and unfair tests of ecological models

Testing ecological models for management is an increasingly important part of the maturation of ecology as an applied science. Consequently, we need to work at applying fair tests of models with adequate data. We demonstrate that a recent test of a discrete time, stochastic model was biased towards falsifying the predictions. If the model was a perfect description of reality, the test falsified the predictions 84% of the time. We introduce an alternative testing procedure for stochastic models, and show that it falsifies the predictions only 5% of the time when the model is a perfect description of reality. The example is used as a point of departure to discuss some of the philosophical aspects of model testing.

Ecosystem process models at multiple scales for mapping tropical forest productivity

Quantifying mass and energy exchanges within tropical forests is essential for understanding their role in the global carbon budget and how they will respond to perturbations in climate. This study reviews ecosystem process models designed to predict the growth and productivity of temperate and tropical forest ecosystems. Temperate forest models were included because of the minimal number of tropical forest models. The review provides a multiscale assessment enabling potential users to select a model suited to the scale and type of information they require in tropical forests. Process models are reviewed in relation to their input and output parameters, minimum spatial and temporal units of operation, maximum spatial extent and time period of application for each organization level of modelling. Organizational levels included leaf-tree, plot-stand, regional and ecosystem levels, with model complexity decreasing as the time-step and spatial extent of model operation increases. All ecosystem models are simplified versions of reality and are typically aspatial. Remotely sensed data sets and derived products may be used to initialize, drive and validate ecosystem process models. At the simplest level, remotely sensed data are used to delimit location, extent and changes over time of vegetation communities. At a more advanced level, remotely sensed data products have been used to estimate key structural and biophysical properties associated with ecosystem processes in tropical and temperate forests. Combining ecological models and image data enables the development of carbon accounting systems that will contribute to understanding greenhouse gas budgets at biome and global scales.

Reconciling paleodistribution models and comparative phylogeography in the Wet Tropics rainforest land snail Gnarosophia bellendenkerensis (Brazier 1875)

Comparative phylogeography has proved useful for investigating biological responses to past climate change and is strongest when combined with extrinsic hypotheses derived from the fossil record or geology. However, the rarity of species with sufficient, spatially explicit fossil evidence restricts the application of this method. Here, we develop an alternative approach in which spatial models of predicted species distributions under serial paleoclimates are compared with a molecular phylogeography, in this case for a snail endemic to the rainforests of North Queensland, Australia. We also compare the phylogeography of the snail to those from several endemic vertebrates and use consilience across all of these approaches to enhance biogeographical inference for this rainforest fauna. The snail mtDNA phylogeography is consistent with predictions from paleoclimate modeling in relation to the location and size of climatic refugia through the late Pleistocene-Holocene and broad patterns of extinction and recolonization. There is general agreement between quantitative estimates of population expansion from sequence data (using likelihood and coalescent methods) vs. distributional modeling. The snail phylogeography represents a composite of both common and idiosyncratic patterns seen among vertebrates, reflecting the geographically finer scale of persistence and subdivision in the snail. In general, this multifaceted approach, combining spatially explicit paleoclimatological models and comparative phylogeography, provides a powerful approach to locating historical refugia and understanding species' responses to them.

A test of the hypothesis of ecological equivalence in an Australian subtropical rain forest

We tested the hypothesis that tree species in a subtropical rain forest in south-east Queensland are ecologically equivalent and therefore have identical environmental requirements for their regeneration. We assessed the evidence that juveniles of species differed in their distributions in treefall gap microsites and along gradients of light availability, soil pH, soil PO4-P availability and soil NO3-N availability. Pairwise comparisons were made on a subset of the common species selected on the basis that they showed a relatively high level of positive association, and would therefore, a priori, be expected to have similar regeneration requirements. Detailed comparisons between the species failed to demonstrate evidence for species differentiation with respect to their tolerance of the disturbance associated with gap microsites or to the gradient of NO3-N availability. However, species differed markedly in their distributions along the soil pH gradient and along the gradients of light availability and soil PO4-P availability. The overall level of ecological differentiation between the species is high: seven out of the 10 possible species pairings showed evidence for ecological differentiation. Such niche differentiation amongst the juveniles of tree species may play an important role in maintaining the species richness of rain-forest communities.

Fitting genetic models to twin data with binary and ordered categorical responses: A comparison of structural equation modelling and Bayesian hierarchical models

We compare Bayesian methodology utilizing free-ware BUGS (Bayesian Inference Using Gibbs Sampling) with the traditional structural equation modelling approach based on another free-ware package, Mx. Dichotomous and ordinal (three category) twin data were simulated according to different additive genetic and common environment models for phenotypic variation. Practical issues are discussed in using Gibbs sampling as implemented by BUGS to fit subject-specific Bayesian generalized linear models, where the components of variation may be estimated directly. The simulation study (based on 2000 twin pairs) indicated that there is a consistent advantage in using the Bayesian method to detect a correct model under certain specifications of additive genetics and common environmental effects. For binary data, both methods had difficulty in detecting the correct model when the additive genetic effect was low (between 10 and 20%) or of moderate range (between 20 and 40%). Furthermore, neither method could adequately detect a correct model that included a modest common environmental effect (20%) even when the additive genetic effect was large (50%). Power was significantly improved with ordinal data for most scenarios, except for the case of low heritability under a true ACE model. We illustrate and compare both methods using data from 1239 twin pairs over the age of 50 years, who were registered with the Australian National Health and Medical Research Council Twin Registry (ATR) and presented symptoms associated with osteoarthritis occurring in joints of the hand.

Is maximizing resilience compatible with established ecological goal functions?

Cropp and Gabric [Ecosystem adaptation: do ecosystems maximise resilience? Ecology. In press] used a simple phytoplanktonzooplankton-nutrient model and a genetic algorithm to determine the parameter values that would maximize the value of certain goal functions. These goal functions were to maximize biomass, maximize flux, maximize flux to biomass ratio, and maximize resilience. It was found that maximizing goal functions maximized resilience. The objective of this study was to investigate whether the Cropp and Gabric [Ecosystem adaptation: do ecosystems maximise resilience? Ecology. In press] result was indicative of a general ecosystem principle, or peculiar to the model and parameter ranges used. This study successfully replicated the Cropp and Gabric [Ecosystem adaptation: do ecosystems maximise resilience? Ecology. In press] experiment for a number of different model types, however, a different interpretation of the results is made. A new metric, concordance, was devised to describe the agreement between goal functions. It was found that resilience has the highest concordance of all goal functions trialled. for most model types. This implies that resilience offers a compromise between the established ecological goal functions. The parameter value range used is found to affect the parameter versus goal function relationships. Local maxima and minima affected the relationship between parameters and goal functions, and between goal functions. (C) 2003 Elsevier B.V. All rights reserved.