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Data Integration for Large-Scale Models of Species Distributions

Authors :
Isaac, N.J.B.
Jarzyna, M.A.
Keil, P.
Dambly, L.I.
Boersch-Supan, P.H.
Browning, E.
Freeman, S.N.
Golding, Nick
Guillera-Arroita, G.
Henrys, P.A.
Jarvis, S.
Lahoz-Monfort, J.
Pagel, J.
Pescott, O.L.
Schmucki, R.
Simmonds, E.G.
O'Hara, R.B.
Isaac, N.J.B.
Jarzyna, M.A.
Keil, P.
Dambly, L.I.
Boersch-Supan, P.H.
Browning, E.
Freeman, S.N.
Golding, Nick
Guillera-Arroita, G.
Henrys, P.A.
Jarvis, S.
Lahoz-Monfort, J.
Pagel, J.
Pescott, O.L.
Schmucki, R.
Simmonds, E.G.
O'Hara, R.B.
Publication Year :
2020

Abstract

With the expansion in the quantity and types of biodiversity data being collected, there is a need to find ways to combine these different sources to provide cohesive summaries of species’ potential and realized distributions in space and time. Recently, model-based data integration has emerged as a means to achieve this by combining datasets in ways that retain the strengths of each. We describe a flexible approach to data integration using point process models, which provide a convenient way to translate across ecological currencies. We highlight recent examples of large-scale ecological models based on data integration and outline the conceptual and technical challenges and opportunities that arise.

Details

Database :
OAIster
Publication Type :
Electronic Resource
Accession number :
edsoai.on1393086489
Document Type :
Electronic Resource