Habitat Requirements of the Mountain Brushtail Possum and the Greater Glider in the Montane Ash-Type Eucalypt Forests of the Central Highlands of Victoria

Lindenmayer, D. B.; Cunningham, RB; Tanton, M. T.; Smith, A. P.; Nix, H. A.

doi:10.1071/wr9900467

Cited by 116 publications

(106 citation statements)

References 10 publications

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“…Most studies utilising the threshold probability approach (Lindenmayer et al, 1990;Pearce et al, 1994) have assumed that the selection of a threshold, such as 0.5, implies that sites with a probability greater than 0.5 will be occupied greater than 50% of the time. However, if a model is not well calibrated and has significant bias or spread error, then a predicted value of 0.5 will not relate to an observed value of 0.5, but a higher or lower observed rate.…”

Section: Resultsmentioning

confidence: 99%

Evaluating the predictive performance of habitat models developed using logistic regression

Pearce

Ferrier

2000

Ecological Modelling

1,673

1,425

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The use of statistical models to predict the likely occurrence or distribution of species is becoming an increasingly important tool in conservation planning and wildlife management. Evaluating the predictive performance of models using independent data is a vital step in model development. Such evaluation assists in determining the suitability of a model for specific applications, facilitates comparative assessment of competing models and modelling techniques, and identifies aspects of a model most in need of improvement. The predictive performance of habitat models developed using logistic regression needs to be evaluated in terms of two components: reliability or calibration (the agreement between predicted probabilities of occurrence and observed proportions of sites occupied), and discrimination capacity (the ability of a model to correctly distinguish between occupied and unoccupied sites). Lack of reliability can be attributed to two systematic sources, calibration bias and spread. Techniques are described for evaluating both of these sources of error. The discrimination capacity of logistic regression models is often measured by cross-classifying observations and predictions in a two-by-two table, and calculating indices of classification performance. However, this approach relies on the essentially arbitrary choice of a threshold probability to determine whether or not a site is predicted to be occupied. An alternative approach is described which measures discrimination capacity in terms of the area under a relative operating characteristic (ROC) curve relating relative proportions of correctly and incorrectly classified predictions over a wide and continuous range of threshold levels. Wider application of the techniques promoted in this paper could greatly improve understanding of the usefulness, and potential limitations, of habitat models developed for use in conservation planning and wildlife management.

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Section: Resultsmentioning

confidence: 99%

Evaluating the predictive performance of habitat models developed using logistic regression

Pearce

Ferrier

2000

Ecological Modelling

1,673

1,425

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“…The results of the study are subjected to logistic regression, and a statistical habitat model constructed (Austin et al 1990, Lindenmayer et al 1990, 1995, Morrison et al 1992. This model identifies habitat or landscape features that are associated with the presence of the species.…”

Section: Effects Of False Negatives On Habitat Modelingmentioning

confidence: 99%

Improving Precision and Reducing Bias in Biological Surveys: Estimating False‐negative Error Rates

Tyre

Tenhumberg

Field

et al. 2003

Ecological Applications

695

738

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Abstract. The use of presence/absence data in wildlife management and biological surveys is widespread. There is a growing interest in quantifying the sources of error associated with these data. We show that false-negative errors (failure to record a species when in fact it is present) can have a significant impact on statistical estimation of habitat models using simulated data. Then we introduce an extension of logistic modeling, the zero-inflated binomial (ZIB) model that permits the estimation of the rate of false-negative errors and the correction of estimates of the probability of occurrence for false-negative errors by using repeated visits to the same site. Our simulations show that even relatively low rates of false negatives bias statistical estimates of habitat effects. The method with three repeated visits eliminates the bias, but estimates are relatively imprecise. Six repeated visits improve precision of estimates to levels comparable to that achieved with conventional statistics in the absence of false-negative errors. In general, when error rates are Յ50% greater efficiency is gained by adding more sites, whereas when error rates are Ͼ50% it is better to increase the number of repeated visits. We highlight the flexibility of the method with three case studies, clearly demonstrating the effect of false-negative errors for a range of commonly used survey methods.

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“…the confusion matrix according to Kohavi and Provost, 1998). The confusion matrix can be used to calculate the indices describing predictive performance of models (Lindenmayer et al, 1990;Pearce et al, 1994): the sensitivity (i.e. the true positive fraction, the proportion of the positive observations in agreement with the presence predictions over the total positive observations) the specificity (i.e.…”

Section: Model Validationmentioning

confidence: 99%

Risk mapping for sensitive species to underwater anthropogenic sound emissions: Model development and validation in two Mediterranean areas

Azzellino

Lanfredi

D’Amico

et al. 2011

Marine Pollution Bulletin

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Azzellino, Arianna; Lanfredi, C.; D' Amico, A.; Pavan, G.; Podestà, M.; and Haun, J., "Risk mapping for sensitive species to underwater anthropogenic sound emissions: Model development and validation in two Mediterranean areas" (2011 a b s t r a c tRecent observations of cetacean mass strandings, coincident with anthropogenic sounds emissions, have raised concerns on the potential environmental impact of underwater noise. Cuvier's beaked whale (Ziphius cavirostris) was reported in all the cited stranding events. Within the NATO Marine Mammal Risk Mitigation project (MMRM), multiple interdisciplinary sea trials have been conducted in the Mediterranean Sea with the objective of developing tools and procedures to mitigate the impact of underwater sound emissions. During these cruises, visual observations, passive acoustic detections and environmental data were collected. The aim of this study was to evaluate ''a priori'' predictions of Cuvier's beaked whale presence in the Alboran Sea, using models developed in the Ligurian Sea that employ bathymetric and chlorophyll features as predictors. The accuracy of these predictions was found adequate and elements are given to account for the uncertainties associated to the use of models developed in areas different from their calibration site.

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Habitat Requirements of the Mountain Brushtail Possum and the Greater Glider in the Montane Ash-Type Eucalypt Forests of the Central Highlands of Victoria

Cited by 116 publications

References 10 publications

Evaluating the predictive performance of habitat models developed using logistic regression

Evaluating the predictive performance of habitat models developed using logistic regression

Improving Precision and Reducing Bias in Biological Surveys: Estimating False‐negative Error Rates

Risk mapping for sensitive species to underwater anthropogenic sound emissions: Model development and validation in two Mediterranean areas

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