Causal inference and large-scale expert validation shed light on the drivers of SDM accuracy and variance

Aim To develop a causal understanding of the drivers of Species distribution model (SDM) performance. Location United Kingdom (UK). Methods We measured the accuracy and variance of SDMs fitted for 518 species of invertebrate and plant in the UK. Our measure of variance reflects variation among repli...

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Veröffentlicht in:	Diversity & distributions 2023-06, Vol.29 (6), p.774-784
Hauptverfasser:	Boyd, Robin J., Harvey, Martin, Roy, David B., Barber, Tony, Haysom, Karen A., Macadam, Craig R., Morris, Roger K. A., Palmer, Carolyn, Palmer, Stephen, Preston, Chris D., Taylor, Pam, Ward, Robert, Ball, Stuart G., Pescott, Oliver L.
Format:	Artikel
Sprache:	eng
Schlagworte:	Accuracy Algorithms causal inference Completeness directed acyclic graph expert elicitation Geographical distribution Graph theory Habitats Model accuracy Niches Regression analysis RESEARCH ARTICLE Species species distribution modelling Statistics Structural equation modeling structural equation modelling Variables Variance analysis
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