Multi-scenario species distribution modeling

Correlative species distribution models (SDMs) are increasingly being used to predict suitable insect habitats. There is also much criticism of prediction discrepancies among different SDMs for the same species and the lack of effective communication about SDM prediction uncertainty. In this paper, we undertook a factorial study to investigate the effects of various modeling components (species-training-datasets, predictor variables, dimension-reduction methods, and model types) on the accuracy of SDM predictions, with the aim of identifying sources of discrepancy and uncertainty. We found that model type was the major factor causing variation in species-distribution predictions among the various modeling components tested. We also found that different combinations of modeling components could significantly increase or decrease the performance of a model. This result indicated the importance of keeping modeling components constant for comparing a given SDM result. With all modeling components, constant, machine-learning models seem to outperform other model types. We also found that, on average, the Hierarchical Non-Linear Principal Components Analysis dimension-reduction method improved model performance more than other methods tested. We also found that the widely used confusion-matrix-based model-performance indices such as the area under the receiving operating characteristic curve (AUC), sensitivity, and Kappa do not necessarily help select the best model from a set of models if variation in performance is not large. To conclude, model result discrepancies do not necessarily suggest lack of robustness in correlative modeling as they can also occur due to inappropriate selection of modeling components. In addition, more research on model performance evaluation is required for developing robust and sensitive model evaluation methods. Undertaking multi-scenario species-distribution modeling, where possible, is likely to mitigate errors arising from inappropriate modeling components selection, and provide end users with better information on the resulting model prediction uncertainty

Novel three-step pseudo-absence selection technique for improving species distribution modelling

Pseudo-absence selection for spatial distribution models (SDMs) is the subject of ongoing investigation. Numerous techniques continue to be developed, and reports of their effectiveness vary. Because the quality of presence and absence data is key for acceptable accuracy of correlative SDM predictions, determining an appropriate method to characterise pseudo-absences for SDM’s is vital. The main methods that are currently used to generate pseudo-absence points are: 1) randomly generated pseudo-absence locations from background data; 2) pseudo-absence locations generated within a delimited geographical distance from recorded presence points; and 3) pseudo-absence locations selected in areas that are environmentally dissimilar from presence points. There is a need for a method that considers both geographical extent and environmental requirements to produce pseudo-absence points that are spatially and ecologically balanced. We use a novel three-step approach that satisfies both spatial and ecological reasons why the target species is likely to find a particular geolocation unsuitable. Step 1 comprises establishing a geographical extent around species presence points from which pseudo-absence points are selected based on analyses of environmental variable importance at different distances. This step gives an ecologically meaningful explanation to the spatial range of background data, as opposed to using an arbitrary radius. Step 2 determines locations that are environmentally dissimilar to the presence points within the distance specified in step one. Step 3 performs K-means clustering to reduce the number of potential pseudo-absences to the desired set by taking the centroids of clusters in the most environmentally dissimilar class identified in step 2. By considering spatial, ecological and environmental aspects, the three-step method identifies appropriate pseudo-absence points for correlative SDMs. We illustrate this method by predicting the New Zealand potential distribution of the Asian tiger mosquito (Aedes albopictus) and the Western corn rootworm (Diabrotica virgifera virgifera).This research was supported by a PhD and writing scholarship award to SS in Lincoln University by the Bio-Protection Research Centre. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Adaptation to hot environmental conditions: an exploration of the performance basis, procedures and future directions to optimise opportunities for elite athletes

Extreme environmental conditions present athletes with diverse challenges; however, not all sporting events are limited by thermoregulatory parameters. The purpose of this leading article is to identify specific instances where hot environmental conditions either compromise or augment performance and, where heat acclimation appears justified, evaluate the effectiveness of pre-event acclimation processes. To identify events likely to be receptive to pre-competition heat adaptation protocols, we clustered and quantified the magnitude of difference in performance of elite athletes competing in International Association of Athletics Federations (IAAF) World Championships (1999-2011) in hot environments (>25 °C) with those in cooler temperate conditions ( 0.8; large impairment), while in contrast, performance in short-duration sprint events was augmented in the heat compared with temperate conditions (~1 % improvement, Cohen's d > 0.8; large performance gain). As endurance events were identified as compromised by the heat, we evaluated common short-term heat acclimation (≤7 days, STHA) and medium-term heat acclimation (8-14 days, MTHA) protocols. This process identified beneficial effects of heat acclimation on performance using both STHA (2.4 ± 3.5 %) and MTHA protocols (10.2 ± 14.0 %). These effects were differentially greater for MTHA, which also demonstrated larger reductions in both endpoint exercise heart rate (STHA: -3.5 ± 1.8 % vs MTHA: -7.0 ± 1.9 %) and endpoint core temperature (STHA: -0.7 ± 0.7 % vs -0.8 ± 0.3 %). It appears that worthwhile acclimation is achievable for endurance athletes via both short-and medium-length protocols but more is gained using MTHA. Conversely, it is also conceivable that heat acclimation may be counterproductive for sprinters. As high-performance athletes are often time-poor, shorter duration protocols may be of practical preference for endurance athletes where satisfactory outcomes can be achieved