To investigate the comparative abilities of six different bioclimatic models in an independent area, utilizing the distribution of eight different species available at a global scale and in Australia. Global scale and Australia. We tested a variety of bioclimatic models for eight different plant species employing five discriminatory correlative species distribution models (SDMs) including Generalized Linear Model (GLM), MaxEnt, Random Forest (RF), Boosted Regression Tree (BRT), Bioclim, together with CLIMEX (CL) as a mechanistic niche model. These models were fitted using a training dataset of available global data, but with the exclusion of Australian locations. The capabilities of these techniques in projecting suitable climate, based on independent records for these species in Australia, were compared. Thus, Australia is not used to calibrate the models and therefore it is as an independent area regarding geographic locations. To assess and compare performance, we utilized the area under the receiver operating characteristic (ROC) curves (AUC), true skill statistic (TSS), and fractional predicted areas for all SDMs. In addition, we assessed satisfactory agreements between the outputs of the six different bioclimatic models, for all eight species in Australia. The modeling method impacted on potential distribution predictions under current climate. However, the utilization of sensitivity and the fractional predicted areas showed that GLM, MaxEnt, Bioclim, and CL had the highest sensitivity for Australian climate conditions. Bioclim calculated the highest fractional predicted area of an independent area, while RF and BRT were poor. For many applications, it is difficult to decide which bioclimatic model to use. This research shows that variable results are obtained using different SDMs in an independent area. This research also shows that the SDMs produce different results for different species; for example, Bioclim may not be good for one species but works better for other species. Also, when projecting a "large" number of species into novel environments or in an independent area, the selection of the "best" model/technique is often less reliable than an ensemble modeling approach. In addition, it is vital to understand the accuracy of SDMs' predictions. Further, while TSS, together with fractional predicted areas, are appropriate tools for the measurement of accuracy between model results, particularly when undertaking projections on an independent area, AUC has been proved not to be. Our study highlights that each one of these models (CL, Bioclim, GLM, MaxEnt, BRT, and RF) provides slightly different results on projections and that it may be safer to use an ensemble of models.

Analyses of knowledge gaps can highlight imbalances in research, encouraging greater proportionality in the distribution of research efforts. In this research we used generalized linear mixed models (GLMM) with the aim to determine if research efforts for the period 2005–2015 for terrestrial vertebrates of Amphibia, Aves, Mammalia and Reptilia in the South Pacific region were correlated with conservation status (critically endangered (CR), endangered (EN), vulnerable (VU), least concern (LC) and near threatened (NT)) or population trends (increasing, stable, decreasing and unknown) through the International Union for Conservation of Nature (IUCN) database. Our results showed that research distribution was uneven across different classes. Out of 633623 investigated papers, the average number of publications per species was 43.7, 306.7, 717.6 and 115.3 for Amphibia (284 species), Aves (1306 species), Mammalia (243 species) and Reptilia (400 species), respectively. Consistently, the lower publication effort on Amphibia compared to other taxonomic classes was revealed as significant by GLMM analysis. There was no significant differences in research effort among levels of conservation status. However, we found significantly different publication efforts among population trends of all examined species in that species with "unknown" population trends gained significantly lower researchers' attention compared to species with "decreasing" trend. Results also indicated that, although it was not significant, the highest attention is given to species with "increasing" population trend over all taxonomic classes. Using the Information Theoretic approach we also generated a set of competing models to identify most important factors influencing research efforts, revealing that the highest ranked model included taxonomic class and population.