Abstract
Abstract. Species distribution models represent an important approach to map the spread of plant and animal species over space (and time). As all the statistical modelling techniques related to data from the field, they are prone to uncertainty. In this study we explicitly dealt with uncertainty deriving from field data sampling; in particular we propose i) methods to map sampling effort bias and ii) methods to map semantic bias.
Highlights
INTRODUCTIONA number of studies have dealt with the prediction of species distribution and diversity over space and its changes over time based on a set of environmental predictors related to environmental variability, productivity, spatial constraints and climate drivers
In ecology, a number of studies have dealt with the prediction of species distribution and diversity over space and its changes over time based on a set of environmental predictors related to environmental variability, productivity, spatial constraints and climate drivers.Species distribution models have been acknowledged as the most powerful methods to map the spread of plant and animal species
The aim of this study is to provide straightforward and robust mapping procedures to explicitly show spatial uncertainty related to sampling problems like sampling effort or crowdsourced semantic uncertainty when dealing with species distribution modelling
Summary
A number of studies have dealt with the prediction of species distribution and diversity over space and its changes over time based on a set of environmental predictors related to environmental variability, productivity, spatial constraints and climate drivers. In case of invasive species it might be crucial to spatially represent uncertainty to allow better decision making. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume II-3/W5, 2015 ISPRS Geospatial Week 2015, 28 Sep – 03 Oct 2015, La Grande Motte, France relation with the following equation: Decision = In this case a high (or low) invasion rate I might be related to high or low error Em in the output model being observed by decision makers. A misconceived use of a species distribution map might be dangerous e.g. in case of a low probability of dispersion of an invasive species but with a high error in the model. The aim of this study is to provide straightforward and robust mapping procedures to explicitly show spatial uncertainty related to sampling problems like sampling effort or crowdsourced semantic uncertainty (relying on commonly used datasets) when dealing with species distribution modelling
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