In pursuit of a more robust provenance in the field of species distribution modelling, an extensive literature search was undertaken to find the typical default values, and the range of values, for configuration settings of a number of the most commonly used statistical algorithms available for constructing species distribution models (SDM), as implemented in the R script packages (such as Dismo and Biomod2) or other species distribution modelling programs like Maxent. We found that documentation of SDM algorithm configuration option settings in the SDM literature is very uncommon, and the justifications for these settings were minimal, when present. Such settings were often the R default values, or were the result of trial and error. This is potentially concerning for a number of reasons; it detracts from the robustness of the provenance for such SDM studies; a lack of documentation of configuration option settings in a paper prevents the replication of an experiment, which contravenes one of the main tenets of the scientific method. Inappropriate or uninformed configuration option settings are particularly concerning if they represent a poorly understood ecological variable or process, and if the algorithm is sensitive to such settings; this could result in erroneous and/or unrealistic SDMs. We test the sensitivity of two commonly used SDM algorithms to variation in configuration options settings: Random Forests and Boosted Regression Trees. A process of expert elicitation was used to derive a range of appropriate values with which to test the sensitivity of our algorithms. We chose to use species occurrence records for the Koala (Phascolartos cinereus) for our sensitivity tests, since the species has a well known distribution. Results were assessed by comparing the geospatial distribution from each sensitivity test (i.e. altered-settings) SDM for differences compared to the control SDM (i.e. default settings), using geographical information systems (QGIS). In addition, two performance measures were used to compare differences among the altered-setting SDMs to the control. The aim of our study was to be able to draw conclusions as to how reliable reported SDM results may be in light of the sensitivity of their algorithms to certain settings, given the often arbitrary nature of such settings, and the lack of awareness of, and/or attendance to this issue in most of the published SDM literature. Our results indicate that all two algorithms tested showed sensitivity to alternate values for some of their settings. Therefore this study has showed that the choice of configuration option settings in Random Forests and Boosted Regression Trees has an impact on the results, and that assigning suitable values for these settings is a relevant consideration and as such should be always published along with the model.

Species distribution modelling (SDM) typically analyses species' presence together with some form of absence information. Ideally absences comprise observations or are inferred from comprehensive sampling. When such information is not available, then pseudo-absences are often generated from the background locations within the study region of interest containing the presences, or else absence is implied through the comparison of presences to the whole study region, e.g. as is the case in Maximum Entropy (MaxEnt) or Poisson point process modelling. However, the choice of which absence information to include can be both challenging and highly influential on SDM predictions (e.g. Oksanen and Minchin, 2002). In practice, the use of pseudo-or implied absences often leads to an imbalance where absences far outnumber presences. This leaves analysis highly susceptible to 'naughty-noughts': absences that occur beyond the envelope of the species, which can exert strong influence on the model and its predictions (Austin and Meyers, 1996). Also known as 'excess zeros', naughty noughts can be estimated via an overall proportion in simple hurdle or mixture models (Martin et al., 2005). However, absences, especially those that occur beyond the species envelope, can often be more diverse than presences. Here we consider an extension to excess zero models. The two-staged approach first exploits the compartmentalisation provided by classification trees (CTs) (as in O'Leary, 2008) to identify multiple sources of naughty noughts and simultaneously delineate several species envelopes. Then SDMs can be fit separately within each envelope, and for this stage, we examine both CTs (as in Falk et al., 2014) and the popular MaxEnt (Elith et al., 2006). We introduce a wider range of model performance measures to improve treatment of naughty noughts in SDM. We retain an overall measure of model performance, the area under the curve (AUC) of the Receiver-Operating Curve (ROC), but focus on its constituent measures of false negative rate (FNR) and false positive rate (FPR), and how these relate to the threshold in the predicted probability of presence that delimits predicted presence from absence. We also propose error rates more relevant to users of predictions: false omission rate (FOR), the chance that a predicted absence corresponds to (and hence wastes) an observed presence, and the false discovery rate (FDR), reflecting those predicted (or potential) presences that correspond to absence. A high FDR may be desirable since it could help target future search efforts, whereas zero or low FOR is desirable since it indicates none of the (often valuable) presences have been ignored in the SDM. For illustration, we chose Bradypus variegatus, a species that has previously been published as an exemplar species for MaxEnt, proposed by Phillips et al. (2006). We used CTs to increasingly refine the species envelope, starting with the whole study region (E0), eliminating more and more potential naughty noughts (E1-E3). When combined with an SDM fit wit...