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Andrew J Hamilton,
Vojtech Novotný,
Edward K Waters,
Yves Basset,
Kurt K Benke,
Peter S Grimbacher,
Scott E Miller,
G Allan Samuelson,
George D Weiblen,
Jian D L Yen,
Nigel E Stork
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ABSTRACT: A key challenge in the estimation of tropical arthropod species richness is the appropriate management of the large uncertainties associated with any model. Such uncertainties had largely been ignored until recently, when we attempted to account for uncertainty associated with model variables, using Monte Carlo analysis. This model is restricted by various assumptions. Here, we use a technique known as probability bounds analysis to assess the influence of assumptions about (1) distributional form and (2) dependencies between variables, and to construct probability bounds around the original model prediction distribution. The original Monte Carlo model yielded a median estimate of 6.1 million species, with a 90 % confidence interval of [3.6, 11.4]. Here we found that the probability bounds (p-bounds) surrounding this cumulative distribution were very broad, owing to uncertainties in distributional form and dependencies between variables. Replacing the implicit assumption of pure statistical independence between variables in the model with no dependency assumptions resulted in lower and upper p-bounds at 0.5 cumulative probability (i.e., at the median estimate) of 2.9-12.7 million. From here, replacing probability distributions with probability boxes, which represent classes of distributions, led to even wider bounds (2.4-20.0 million at 0.5 cumulative probability). Even the 100th percentile of the uppermost bound produced (i.e., the absolutely most conservative scenario) did not encompass the well-known hyper-estimate of 30 million species of tropical arthropods. This supports the lower estimates made by several authors over the last two decades.
Oecologia 09/2012; · 3.41 Impact Factor
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The American Naturalist 04/2011; 177(4):544-5. · 4.72 Impact Factor
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ABSTRACT: There is a bewildering range of estimates for the number of arthropods on Earth. Several measures are based on extrapolation from species specialized to tropical rain forest, each using specific assumptions and justifications. These approaches have not provided any sound measure of uncertainty associated with richness estimates. We present two models that account for parameter uncertainty by replacing point estimates with probability distributions. The models predict medians of 3.7 million and 2.5 million tropical arthropod species globally, with 90% confidence intervals of [2.0, 7.4] million and [1.1, 5.4] million, respectively. Estimates of 30 million or greater are predicted to have <0.00001 probability. Sensitivity analyses identified uncertainty in the proportion of canopy arthropod species that are beetles as the most influential parameter, although uncertainties associated with three other parameters were also important. Using the median estimates suggests that in spite of 250 years of taxonomy and around 855,000 species of arthropods already described, approximately 70% await description.
The American Naturalist 07/2010; 176(1):90-5. · 4.72 Impact Factor
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ABSTRACT: Cocoa Pod Borer (Conopomorpha cramerella Snellen) (CPB) is an important pest of cocoa. Following its emergence as a pest in East New Britain, Papua New Guinea, in 2006, it was considered relevant to assess its potential spread to other cocoa growing regions. Its likelihood of introduction to the islands of Bougainville and New Ireland from East New Britain Province, Papua New Guinea, was modeled using Monte Carlo simulation. This dispersal model was based around different scenarios, identifying trends rather than explicitly attempting to encapsulate true values. The model suggested that CPB is far more likely to establish on New Ireland than on Bougainville. More important, incertitude resulting from incomplete knowledge of the amount and frequency of cocoa transported between islands had a significant effect on model outputs. Quarantine and agriculture officials will be able to refine these parameter values, and then use the relevant scenarios from those presented here as a guide to develop quarantine procedures. In addition, a contingency model was employed to estimate the optimal sampling effort to use following an incursion of CPB into Bougainville or New Ireland and the seemingly successful implementation of an initial eradication program. The model suggests that at a 1% infestation level, sampling should continue for 2.5-2.7 years (90% CI) after claiming eradication, and this estimate changed little for higher infestation levels. Through modeling variations in sampling intensity, the model also suggested that determining the full spread of CPB is more important than increased sampling within one region.
Risk Analysis 09/2009; 30(2):293-309. · 2.37 Impact Factor
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ABSTRACT: Analysis of uncertainty is often neglected in the evaluation of complex systems models, such as computational models used in hydrology or ecology. Prediction uncertainty arises from a variety of sources, such as input error, calibration accuracy, parameter sensitivity and parameter uncertainty. In this study, various computational approaches were investigated for analysing the impact of parameter uncertainty on predictions of streamflow for a water-balance hydrological model used in eastern Australia. The parameters and associated equations which had greatest impact on model output were determined by combining differential error analysis and Monte Carlo simulation with stochastic and deterministic sensitivity analysis. This integrated approach aids in the identification of insignificant or redundant parameters and provides support for further simplifications in the mathematical structure underlying the model. Parameter uncertainty was represented by a probability distribution and simulation experiments revealed that the shape (skewness) of the distribution had a significant effect on model output uncertainty. More specifically, increasing negative skewness of the parameter distribution correlated with decreasing width of the model output confidence interval (i.e. resulting in less uncertainty). For skewed distributions, characterisation of uncertainty is more accurate using the confidence interval from the cumulative distribution rather than using variance. The analytic approach also identified the key parameters and the non-linear flux equation most influential in affecting model output uncertainty.
Mathematical and Computer Modelling. 01/2008;
