Continental‐scale 1 km hummingbird diversity derived from fusing point records with lateral and elevational expert information. Issue 4 (2nd February 2021) - Ellis‐Soto, Diego; Merow, Cory; Amatulli, Giuseppe; Parra, Juan L.; Jetz, Walter
Abstract : Anthropogenic change is affecting mountain regions worldwide. Managing this change and advancing biodiversity information for research requires spatially detailed information on species distributions which often is incomplete. Here, we provide a model‐based approach for the integration of expert‐based elevational range information with expert range maps and point occurrences to address this need. These integrated models use expert knowledge on elevational and distributional ranges as offset in a Poisson point process species distribution model (SDM). We use this approach to model the distribution of 276 hummingbird Trochilidae species at 1 km resolution and validate model performance with extensive survey data (presence–absence inventories). Models including expert elevation information consistently outperformed those lacking this information. Improvements were greatest when the number of available occurrences was small, highlighting the added value from expert elevation information, especially for data‐poor species. Separate validation data indicated significant increases in true skill statistics, based on higher specificity and slightly improved sensitivity. SDMs that included expert range information out‐performed presence‐only models based only on occurrence data in 92.5% of cases and had higher sensitivity, lower false positive rates and smaller predicted range sizes. Generally, the integrated models removed unsuitable areas from range estimates and decreased overestimates in geographic range size (false presences) inherent in expert maps and in models lacking elevation information. By stacking SDM output we provide a first, hemispheric map of predicted hummingbird species richness modelled at 1 km resolution and identify southwest Colombia as a richness hotspot. Our study highlights the value gained from integrating multiple data types in a single framework. The presented approach improved high‐resolution range predictions for single species (reducing false presences) and aggregate biodiversity patterns (e.g. reducing species richness overestimates). The method is now being implemented and expanded in Map of Life.