Species distribution models (SDMs) can be important tools for proactive conservation management if they are realistic. Unfortunately, achieving and assessing SDM realism is challenging given the general limitations of scientific models and empirical species data. We addressed the issue of achieving realism with high model quality and reproducibility by reviewing 200 SDMs and cataloguing methods for data availability, response and predictor variables, model fitting, and model performance. We addressed the issue of assessing SDM realism by comparing known and predicted distributions of habitat suitability with simulated data for various model fitting choices. Finally, we applied and compared subsequent lessons to empirical, ensemble SDMs for the exotic ball python (Python regius) and invasive Argentine black and white tegu (Salvator merianae) as case studies for Florida mitigation management practices. Fundamental SDM standards were addressed inconsistently in the literature and lacked transparency and replicability. This decreases SDM quality and increases method confusion. We provided a new checklist with well-supported guidelines to aid in greater method consistency (thus quality and reproducibility) and realism. Model realism varied based on algorithm choice but was consistent across sample sizes and species types. No algorithm was perfectly realistic, but eight consistently produced high rates of realism and performance (and the two were not strongly correlated). Ensemble strategies were consistently more robust than individual algorithms, so we recommended a new ensemble based on those eight high-performing algorithms. We applied this ensemble strategy to our empirical SDMs along with other ensemble groupings (including the most popular individual algorithm) from the literature to inform novel SDMs. Ensemble SDMs consistently performed well with the empirical data and outperformed the individual algorithm. Results here help inform general SDM method guidance for a variety of native and nonnative species (with both simulated and empirical demonstrations) to improve SDM realism and applications in the future.

abstract: This work investigates the effects of non-random sampling on our understanding of species distributions and their niches. In its most general form, bias is systematic error that can obscure interpretation of analytical results by skewing samples away from the average condition of the system they represent. Here I use species distribution modelling (SDM), virtual species, and multiscale geographically weighted regression (MGWR) to explore how sampling bias can alter our perception of broad patterns of biodiversity by distorting spatial predictions of habitat, a key characteristic in biogeographic studies. I use three separate case studies to explore: 1) How methods to account for sampling bias in species distribution modeling may alter estimates of species distributions and species-environment relationships, 2) How accounting for sampling bias in fossil data may change our understanding of paleo-distributions and interpretation of niche stability through time (i.e. niche conservation), and 3) How a novel use of MGWR can account for environmental sampling bias to reveal landscape patterns of local niche differences among proximal, but non-overlapping sister taxa. Broadly, my work shows that sampling bias present in commonly used federated global biodiversity observations is more than enough to degrade model performance of spatial predictions and niche characteristics. Measures commonly used to account for this bias can negate much loss, but only in certain conditions, and did not improve the ability to correctly identify explanatory variables or recreate species-environment relationships. Paleo-distributions calibrated on biased fossil records were improved with the use of a novel method to directly estimate the biased sampling distribution, which can be generalized to finer time slices for further paleontological studies. Finally, I show how a novel coupling of SDM and MGWR can illuminate local differences in niche separation that more closely match landscape genotypic variability in the two North American desert tortoise species than does their current taxonomic delineation. / Dissertation/Thesis / Doctoral Dissertation Geography 2018