Effects of input data sources on species distribution model predictions across species with different distributional ranges

Abstract

Aim A major source of uncertainty in the application of species distribution models (SDMs) is related to input data quality. Citizen‐collected species occurrence data are often used for fitting SDMs when data from standardized and expert‐supported surveys are unavailable. Macroclimate variables are much more commonly used as predictors in SDMs than other sources coming from remote sensing data. Here, we assess the effects of using different data sources (in both response and predictor variables) on SDM performance across a wide range of species with contrasting distributional ranges. Location Iberian Peninsula. Taxon Birds. Methods A SDM ensemble‐forecasting approach was implemented using bird data from two different data sources: the eBird project and Atlases. We fitted SDMs with three predictor types: macroclimate, remotely sensed ecosystem functional attributes (EFAs) and their combination. Species were grouped in four range size classes. We assessed the uncertainty of model predictions by different evaluation metrics. Generalized linear mixed‐effects models tested the effect on model performance of input data source across distributional range sizes while accounting for different accuracy metrics. Pairwise comparisons between range projections were used to assess their spatial similarity. Results Data source, size class, predictor and accuracy metric showed significant effects on SDM performance. eBird‐based models outperformed those built with Atlas data for less widespread species. Climate predictors yielded models with the best performance, especially when combined with EFAs. However, the predictor contribution was consistent across bird datasets, being mostly driven by the species range. Main Conclusions Our models demonstrated the usefulness and complementarity of different input data sources when modelling species distribution across different distributional ranges. These findings highlight the need to integrate different data sources to improve the model predictions at regional scale. Our framework also underlines that model uncertainty should be examined more exhaustively at early stages of the modelling process.

Climate Change and Human Activities, the Significant Dynamic Drivers of Himalayan Goral Distribution (Naemorhedus goral)

“…SDM methods have recently been utilized in fields such as invasion biology, restoration ecology, and conservation ecology to forecast hotspots for the protection and cultivation of endemic and threatened taxa [ 11 , 12 ]. The SDM method, based on the niche conservatism theory, predicts the distribution of species along spatio-temporal gradients using a combination of climatic and other environmental variables with data on species distribution [ 13 ]. The influence of future climate change on a region of appropriate habitat for a species is frequently predicted using the niche modeling technique known as the MaxEnt (maximum entropy) model [ 14 ].…”

Sampling from commercial vessel routes can capture marine biodiversity distributions effectively

“…RF was run with 5000 regression trees and a terminal node of 5 (Zhang et al, 2019 ). We randomly assigned the dataset into training (70%) and testing (30%) sets three times for cross‐validation (Arenas‐Castro et al, 2022 ; Sundaram & Leslie, 2021 ). We assembled the model projections across the three modeling methods using weighted AUC scores for each species.…”

Sampling from commercial vessel routes can capture marine biodiversity distributions effectively

“…RF was run with 5000 regression trees and a terminal node of 5 (Zhang, L. et al, 2019). We randomly assigned the data set into training (70 %) and testing (30 %) sets three times for cross validation (Sundaram and Leslie, 2021; Arenas-Castro et al, 2022). We assembled the model projections across the three modelling methods using weighted AUC scores for each species.…”