A model of niche overlap and interaction in ecological systems

Ecological niches are the result of ecosystem element (compartment) interaction under the influence of environmental factors. The competence model is devised to map this interaction. It includes the assumption of an overall physiological state of a compartment depending only on intensive environmental factors, expressions for the adaptedness and competence of the compartment in interaction. Consequently, niches are environmental factor domains of superior competence. The situation of niches (niche structure) and competence maxima are extensively studied in the fundamental case of two interacting compartments and in the special case of n concentric compartments. The impact of one immigrating compartment is discussed. All detailed investigations made involve only one intensive environmental factor and the special assumption of similar physiological state functions of all compartments.     

Ecological niche modelling of an invasive alien plant and its potential biological control agents

Invasive alien plants are of concern in South Africa. Pompom weed (Campuloclinium macrocephalum) is currently invading the Grassland and Savannah biomes of South Africa and is likely to continue spreading in the southern African sub- region. Two possible biological control agents (Liothrips tractabilis and Cochylis campuloclinium) have been identified for control of pompom weed. We used ecological niche modelling to predict which areas in southern Africa are likely to be suitable for pompom weed and the two potential biological control agents. The overlap between areas predicted to be highly suitable for pompom weed and areas suitable for the biological control agents was assessed. Methods of reducing sampling bias in a data set used for calibrating models were also compared. Finally, the performance of models calibrated using only native range data, only invaded range data and both were also compared. Models indicate that pompom weed is likely to spread across a greater region of southern Africa than it currently occupies, with the Savannah and Grassland biomes being at greatest risk of invasion. Poor overlap was found between the areas predicted to be highly suitable for pompom weed and those areas predicted to be suitable for the biological control agents. However, models of the potential distribution of the biological control agents are interpreted with caution due to the very small sample size of the data set used to calibrate the models. Models calibrated using both native range and invaded range data were found to perform best whilst models calibrated using only native range data performed the worst. There was little difference found between models that were calibrated using spatially reduced (selecting only one record per 30 min grid cell) and randomly reduced (randomly selecting 50% of available records) biased data sets.     

Ecological niche modeling of the sympatric giant and red pandas on a mountain-range scale

Habitat use and separation between the two sympatric species, the giant panda and the red panda, have been primary causes of coexistence at the fine scale. In this paper, we addressed the question of coexistence between species in space. By Ecological Niche Factor Analysis, we calculated species-specific habitat requirements, built habitat suitability maps and examined interspecific differences in spatial niche parameters. According to the ENFA scores, suitable habitats in the giant and red panda are surrounded by high-altitude, and are rich in conifer forest. Compared with the giant panda, however, the red panda rather preferred sparse forests, and normally colonized far from village and road. Despite similar narrow niche breadth for both pandas, difference of niche overlap indices implied that the width of environmental niche of red pandas almost completely encompasses that of the giant panda. We, therefore, suggest that differences in use of ecological niche variables may contribute to coexistence of the sympatric species in space. Based on highly suitable locations of the sympatric species maps, most official reserves appear to be poorly located or are too small, and new reserves are recommended to be established in the central part of core habitats in the Liangshan Mountains.     

Mapping the geographic distribution of <Emphasis Type="Italic">Aglaia bourdillonii</Emphasis> Gamble (Meliaceae), an endemic and threatened plant,using ecological niche modeling

Aglaia bourdillonii is a plant narrowly endemic to the southern portion of the Western Ghats (WG), in peninsular India. To understand its ecological and geographic distribution, we used ecological niche modeling (ENM) based on detailed distributional information recently gathered, in relation to detailed climatic data sets. The ENMs successfully reconstructed key features of the species’ geographic distribution, focusing almost entirely on the southern WG. Much of the species’ distributional potential is already under protection, but our analysis allows identification of key zones for additional protection, all of which are adjacent to existing protected areas. ENM provides a useful tool for understanding the natural history of such rare and endangered species.

A hybrid model for reducing ecological bias

A major drawback of epidemiological ecological studies, in which the association between area-level summaries of risk and exposure is used to make inference about individual risk, is the difficulty in characterizing within-area variability in exposure and confounder variables. To avoid ecological bias, samples of individual exposure/confounder data within each area are required. Unfortunately, these may be difficult or expensive to obtain, particularly if large samples are required. In this paper, we propose a new approach suitable for use with small samples. We combine a Bayesian nonparametric Dirichlet process prior with an estimating functions' approach and show that this model gives a compromise between 2 previously described methods. The method is investigated using simulated data, and a practical illustration is provided through an analysis of lung cancer mortality and residential radon exposure in counties of Minnesota. We conclude that we require good quality prior information about the exposure/confounder distributions and a large between- to within-area variability ratio for an ecological study to be feasible using only small samples of individual data.     

A comparison of approaches for modelling the occurrence of marine animals

Approaches for modelling the distribution of animals in relation to their environment can be divided into two basic types, those which use records of absence as well as records of presence and those which use only presence records. For terrestrial species, presence–absence approaches have been found to produce models with greater predictive ability than presence-only approaches. This study compared the predictive ability of both approaches for a marine animal, the harbour porpoise (Phoceoena phocoena). Using data on the occurrence of harbour porpoises in the Sea of Hebrides, Scotland, the predictive abilities of one presence–absence approach (generalised linear modelling—GLM) and three presence-only approaches (Principal component analysis—PCA, ecological niche factor analysis—ENFA and genetic algorithm for rule-set prediction—GARP) were compared. When the predictive ability of the models was assessed using receiver operating characteristic (ROC) plots, the presence–absence approach (GLM) was found to have the greatest predictive ability. However, all approaches were found to produce models that predicted occurrence significantly better than a random model and the GLM model did not perform significantly better than ENFA and GARP. The PCA had a significantly lower predictive ability than GLM but not the other approaches. In addition, all models predicted a similar spatial distribution. Therefore, while models constructed using presence–absence approaches are likely to provide the best understanding of species distribution within a surveyed area, presence-only models can perform almost as well. However, careful consideration of the potential limitations and biases in the data, especially with regards to representativeness, is needed if the results of presence-only models are to be used for conservation and/or management purposes. Guest editor: V. D. Valavanis Essential Habitat Mapping in the Mediterranean     

Lutzomyia vectors for cutaneous leishmaniasis in Southern Brazil: ecological niche models,predicted geographic distributions,and climate change effects

Geographic and ecological distributions of three Lutzomyia sand flies that are cutaneous leishmaniasis vectors in South America were analysed using ecological niche modelling. This new tool provides a large-scale perspective on species' geographic distributions, ecological and historical factors determining them, and their potential for change with expected environmental changes. As a first step, the ability of this technique to predict geographic distributions of the three species was tested statistically using two subsampling techniques: a random-selection technique that simulates 50% data density, and a quadrant-based technique that challenges the method to predict into broad unsampled regions. Predictivity under both test schemes was highly statistically significant. Visualisation of ecological niches provided insights into the ecological basis for distributional differences among species. Projections of potential geographic distributions across scenarios of global climate change suggested that only Lutzomyia whitmani is likely to be experiencing dramatic improvements in conditions in south-eastern Brazil, where cutaneous leishmaniasis appears to be re-emerging; Lutzomyia intermedia and Lutzomyia migonei may be seeing more subtle improvements in climatic conditions, but the implications are not straightforward. More generally, this technique offers the possibility of new views into the distributional ecology of disease, vector, and reservoir species.