Potential habitat distribution for the freshwater diatom Didymosphenia geminata in the continental US

Abstract

The diatom Didymosphenia geminata is a single‐celled alga found in lakes, streams, and rivers. Nuisance blooms of D geminata affect the diversity, abundance, and productivity of other aquatic organisms. Because D geminata can be transported by humans on waders and other gear, accurate spatial prediction of habitat suitability is urgently needed for early detection and rapid response, as well as for evaluation of monitoring and control programs. We compared four modeling methods to predict D geminata's habitat distribution; two methods use presence–absence data (logistic regression and classification and regression tree [CART]), and two involve presence data (maximum entropy model [Maxent] and genetic algorithm for rule‐set production [GARP]). Using these methods, we evaluated spatially explicit, bioclimatic and environmental variables as predictors of diatom distribution. The Maxent model provided the most accurate predictions, followed by logistic regression, CART, and GARP. The most suitable habitats were predicted to occur in the western US, in relatively cool sites, and at high elevations with a high base‐flow index. The results provide insights into the factors that affect the distribution of D geminata and a spatial basis for the prediction of nuisance blooms.

Potential, realised, future distribution and environmental suitability for Pterocarpus angolensis DC in southern Africa

“…Maxent is a general-purpose machine learning method, which uses a maximum-entropy approach. Its application in species distribution modelling is fairly recent and gives superior results compared to other methods (Elith et al, 2006;Kumar et al, 2009;Phillips et al, 2006Phillips et al, , 2004Poulos et al, 2012). The prediction of the model indicates the areas within the study region that satisfy the requirements of the species' ecological niche.…”

Habitat distribution modeling reveals vegetation flammability and land use as drivers of wildfire in SW Patagonia

“…This algorithm uses presence-only data of the variable of interest (i.e., burned polygons) to compare the values of the environmental predictors associated with these presence points with those of a background consisting of the means of all values of the environmental variables over the entire study area. MaxEnt has proven to be more conservative than other models in predicting probability of species occurrence (Kumar et al 2009), in part, because it adjusts for over fitting through a process of ''regularization,'' which prevents the algorithm from matching the observations too closely (Elith et al 2011).…”

Mapping Invasive Tamarisk (Tamarix): A Comparison of Single-Scene and Time-Series Analyses of Remotely Sensed Data

“…Because conventional methods of remote sensing have limited applications for detecting tamarisk, our analyses were conducted using the Maxent software (v3.2.1; www.cs.princeton.edu/~schapire/maxent/), which uses presence points to predict the potential range and habitat distribution of a species [36,37]. In several recent studies, Maxent has been found to be especially useful for mapping invasive species [38,39] and ranked high when compared to other models for predicting tamarisk distributions [35]. We tested six satellite scenes and derived vegetation indices from different months of the growing season to detect tamarisk using single-scene and time-series analyses.…”