By comparing existent models, some ecologists found that complicated modeling techniques are more robust in terms of realized distribution modeling, and that predictions are usually more reliable for the species with smaller range sizes and higher habitat specifity. Jimenez-Valverde, Lobo, and Hortal argued that the interpretation of modeling results found in the comparisons above would vary if methodological and theoretical considerations are taken into consideration. They mentioned three important topics that need to be taken into consideration when conducting species distribution modeling: 1) the distribution between potential and realized distribution, 2) the effect of the relative occurrence area of the species on the results of the model performance, and 3) the inaccuracy of the resulted prediction of the realized distribution from different modeling methods. They reviewed most recent papers applying SDMs and discuess the negative implications of neglecting the three issues mentioned above, targeting on two general conclusions from other comparison papers:
– Are complex techniques better for the prediction of species distributions than simple ones?
With the bias shown by most of the biodiversity inventories, any comparisons among presence-only modeling generally provide distribution close to the potential. A more complex technique tends to overfit the presence data. When validate it with true absence data, it can be erroneously concluded that the predictions from the complex one are more accurate.
– Are the predictions fro specialist species more reliable than for generalists?
Jimenez-Valverde et.al. argued that the seemingly better predictions for specialists are usually the result of the properties of the data used for validation. Besides, rare/specialists and common/generalists gradients are extent- and scale- dependent. They found models of rare species are inevitably with high discrimination, which would be either over or underestimate the distribution of the species.
Their conclusion is that a solid conceptual and methodological framework is necessary for future works evaluating, comparing, and applying species distribution modeling techniques. This paper is inspiring since it provided alternative explanations for universally admitted conclusions. It will be more convincing that they can have species example as supports for their theoretical arguments. In addition, they “deliberately avoided using the term niche to refer to species distributions” due to the recognition that species can be absent from suitable habitat and/or present in unsuitable ones. So they clarified that they were only talking about statistical models, which are not able to provide a description if species niche. It is kind of ironic that they supported a “better understanding of basic concepts for any species modeling or methods comparison” on one hand, and avoided to talk about the most basic concepts behind distribution theory on the other hand.